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All Journal Jurnal Ilmiah Informatika Komputer JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) JURTEKSI Indonesian Journal of Applied Informatics Informasi Interaktif Jurnal Teknologi Informasi dan Multimedia Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Innovation and Future Technology (IFTECH) Zonasi: Jurnal Sistem Informasi Respati JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Information System Journal (INFOS) Journal of Computer Science and Informatics Engineering Batara Wisnu : Indonesian Journal of Community Services Jurnal Teknologi Informasi SWAGATI: Journal of Community Service Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian Masyarakat Inovasi Indonesia
Fajri, Ika Nur
Universitas AMIKOM Yogyakarta
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Other

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Penerapan Algoritma K-Nearest Neighbor (KNN) Untuk Klasifikasi Resiko Penyakit Jantung Dari, Aprillia Wulan Nanda; Fajri, Ika Nur
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6038

Abstract

Heart disease is one of the deadliest diseases in the world, where there is a disruption in the function of the heart and blood vessels that causes chest pain, irregular heartbeat, and difficulty breathing. According to data from the World Health Organization (WHO), there are 17.9 million deaths each year due to heart disease. The difficulty in classifying heart disease accurately and quickly is a significant problem. From this problem, researchers conducted data mining research using the KNN algorithm to classify the risk of heart disease by taking data from the official Kaggle website. In this study, there are 4 stages, namely data collection, model formation, mode evaluation, and prediction interface. By using the KNN algorithm, the analysis results obtained an accuracy of 83%, precision 0.88, recall 0.77 and f1-score 0.82. With the results of the model evaluation data, it shows that the classification of heart disease risk using the KNN algorithm has quite good performance. The results of the modeling are then presented in the form of a website by deploying the model.
Pembuatan Media Promosi Online Berupa Website pada Gutera Olah Pangan Kurniawan, Hendra; Fajri, Ika Nur
SWAGATI : Journal of Community Service Vol. 1 No. 2 (2023): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2023v1i2.1146

Abstract

Pasca pendemi Covid-19 yang terjadi di Indonesia telah banyak merubah gaya promosi para pelaku usaha. Banyak pelaku usaha UMKM (Usaha Mikro, Kecil, dan Menengah) yang terdorong untuk memanfaatkan teknologi sebagai media promosi online agar mampu bersaing. Gutera Olah Pangan sebagai mitra merupakan UMKM yang bergerak di bidang makanan yang telah mempunyai produk berupa sari kacang hijau, olahan kacang, buah beku, katering, dan berbagai minuman buah. Menurut data dari BPS (Badan Pusat Statistik) tahun 2020 bahwa kendala terbesar UMKM adalah pemasaran atau penjualan produk dengan prosentase 48,60%. Kendala ini juga dialami oleh mitra yang sulit melakukan penjualan produk yang disebabkan oleh banyaknya produk makanan dan kencenderungan masyarakat memilih produk murah. Upaya promosi menggunakan marketplace dan media sosial telah dilakukan, tetapi belum berdampak signifikan terhadap penjualan. Oleh karena itu, dibutuhkan upaya lain berupa pemanfaatan website agar dapat mendukung kegiatan promosi yang telah dilakukan oleh mitra dan membangun brand image di masyarakat.
IMPLEMENTASI QUICK RESPONSE CODE UNTUK PENDUKUNG SISTEM INFORMASI PRESENSI Nur Indah Kusumawardhani; Ika Nur Fajri
Jurnal Ilmiah Informatika Komputer Vol 29, No 3 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i3.12189

Abstract

The development of science and technology (IPTEK) and information is a reality that must be faced by everyone, including educational institutions and government agencies. One example is the use of information technology in schools to facilitate the work of educators and education personnel. However, in primary, secondary, and higher education, many still use manual attendance. SMK Negeri 2 Klaten also experienced this problem. To overcome this problem, a website was designed to record, report, and monitor student attendance using the QR Code scanning method. This research uses the waterfall method which includes Requirements Analysis, System Design, Implementation, Integration and Testing, and Operation and Maintenance. The results showed that a website-based attendance information system can speed up and simplify the attendance process, reduce errors, and increase efficiency. It is proven that after testing the QR Code based on the scanning distance, the scanning response speed only requires a delay of 0.94 seconds at an effective distance of 20 cm. In conclusion, the application of information technology in the attendance process at SMK Negeri 2 Klaten can improve the quality and efficiency of attendance management.
Liver Disease Classification using the NAIVE BAYES Nurhalisa, Vitra; Fajri, Ika Nur
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): 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.v14i4.5072

Abstract

The advancement of artificial intelligence technology presents new opportunities to support medical professionals in making faster and more accurate clinical decisions. This study introduces a liver disease classification system based on the Naive Bayes algorithm, designed to be easily interpretable by doctors and healthcare personnel. A dataset of 580 patients with 11 clinical attributes—ranging from bilirubin levels to albumin–globulin ratio—was used and processed through data cleaning and normalization stages. The Bernoulli Naive Bayes model was then trained and evaluated using a confusion matrix and ROC-AUC analysis. The results show an accuracy of 67%, with strong performance in identifying patients at risk of liver disease (recall of 0.82), but weaker in classifying healthy individuals (recall of 0.28). The fast training time and transparent probabilistic predictions of the Naive Bayes algorithm make it a practical solution for developing a prototype of a medical decision support system. Future recommendations include incorporating additional relevant clinical features and applying ensemble methods to improve diagnostic sensitivity and specificity.
Comparison of K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) Algorithms in Predicting Customer Satisfaction Pratama, Subhan Rizky; Fajri, Ika Nur
Journal of Computer Science and Informatics Engineering Vol 4 No 3 (2025): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i3.1160

Abstract

This study compares the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in predicting customer satisfaction at Warung Makan Indomie (Warmindo). The research process consists of four stages, namely: data collection, data processing, model formation, and model evaluation. This study aims to compare the performance of two classification algorithms, namely K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), in predicting customer satisfaction levels based on survey data. The evaluation was carried out using accuracy metrics and classification reports to determine the level of precision, recall, and f1-score of each algorithm. The evaluation results show that both algorithms have the same accuracy of 70%. KNN excels in f1-score in class 2 (0.70), while SVM excels in precision in class 2 (0.79). with an average score of both algorithms being 0.61. These results indicate that both KNN and SVM are feasible to use, depending on the performance priority per class
Pengembangan Sistem Informasi Pendakian Gunung “AyoMuncak” Berbasis Website dengan Pemanfaatan Data Geospasial Hijriah, Az Zahra; Fajri, Ika Nur; Nugroho, Agung
Jurnal Teknologi Informasi Vol 4, No 1 (2025): Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/juti.v4i1.2125

Abstract

The increasing public interest in mountain hiking tourism in Indonesia has not been fully supported by the availability of accurate and integrated hiking information. This study aims to develop a web-based mountain hiking information system utilizing geospatial data to provide centralized and interactive information on hiking routes, weather forecasts, and hiker experiences. The system, named AyoMuncak, integrates interactive maps using Leaflet.js and weather data from the OpenWeatherMap API, and supports user-generated reviews. The system was developed using the waterfall model, which includes communication, planning, modeling, construction, and testing phases. Black box testing was used to ensure functional requirements were met. The results show that the system successfully delivers comprehensive information about 26 mountains in East Java, featuring mountain lists, location maps, weather forecasts, and review management. The system has been tested and proven to meet both user and admin needs. It is expected to enhance safety, convenience, and trip planning for hikers, while promoting the digitalization of tourism services based on spatial data.
Analysis and Design of Sales Website at Twins Petshop Using the Waterfall Method Pinasti, Rafa Hadiya; Fajri, Ika Nur
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.29

Abstract

The pet shop industry continues to grow as people's interest in pets increases. However, many petshops face challenges in managing products and transactions that are still done manually. This is also experienced by Twins petshop, which still uses manual methods in managing product and transaction data, thus hindering data operational efficiency and market reach that has not been maximized. To overcome this problem, this study was made with the aim of designing and developing a website-based petshop sales information system, thereby helping to improve the efficiency of product and transaction data management. The development method used is the waterfall method which consists of several stages that must be carried out in stages, namely needs analysis, design, implementation, and testing. The tests are carried out using the balck-box testing method to ensure that all features run according to user needs. The results of the balckbox test show that of the eight scenarios tested, all succeeded with a 100% success percentage. Scenarios include admin logins with valid and invalid data, data editing and deletion, and adding products with invalid forms. The results of this study show that the website developed is able to increase the efficiency of product recording, transactions, and provide more complete information than the previous manual system.
Generative AI Image Sentiment Analysis on Social Media X using TF-IDF and FastText Saputra, Rahman; Pristyanto, Yoga; Fajri, Ika Nur
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10627

Abstract

This research investigates public opinion on AI-generated images on Social Media X using machine learning-driven text classification. Three classification models were evaluated: Complement Naïve Bayes (CNB) utilizing TF-IDF features, Support Vector Machine (SVM) merging TF-IDF with FastText embeddings, and IndoBERT as a modern transformer-based baseline. A total of 1,958 Indonesian tweets were collected via web scraping with relevant keywords, followed by a pipeline involving text cleaning, manual labeling into positive, negative, and neutral categories, and data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) for the classical models (with class weighting applied for IndoBERT). Results show that the SVM model outperformed the others, achieving 68.7% accuracy with average precision, recall, and F1-score of 0.69, 0.69, and 0.68, respectively; CNB attained 64.1% accuracy with average metrics of 0.64; while IndoBERT recorded 58.2% accuracy with average precision, recall, and F1-score of 0.58, 0.58, and 0.57. Confusion matrix analysis revealed SVM's superior ability to distinguish positive and neutral sentiments in casual language, though IndoBERT demonstrated potential for capturing deeper semantic nuances despite underperforming due to dataset size and informal text. The findings highlight the efficacy of integrating statistical and semantic representations for improved sentiment analysis on unstructured, noisy social media data related to AI-generated imagery, while suggesting that transformer models like IndoBERT may benefit from larger datasets for optimal performance.
Sentiment Classification Analysis of Tokopedia Reviews Using TF-IDF, SMOTE, and Traditional Machine Learning Models Barus, Herianta; Fajri, Ika Nur; Pristyanto, Yoga
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10524

Abstract

This study explores sentiment classification on Tokopedia user reviews using TF-IDF for feature extraction and SMOTE to handle class imbalance. From nearly one million raw reviews sourced from Kaggle ("E-Commerce Ratings and Reviews in Bahasa Indonesia"), a final set of 6,477 relevant entries was obtained after rigorous preprocessing, including case folding, noise removal (emojis, URLs, numbers), normalization to KBBI standards, tokenization, stopword removal, and stemming with Sastrawi. The dataset consisted of 5,213 positive and 1,264 negative reviews (80.4% positive). SMOTE balanced the classes to 10,426 reviews with a 1:1 ratio for training. Five traditional machine learning models were evaluated: Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Decision Tree, and Random Forest. Assessments were based on accuracy, precision, recall, F1-score, ROC-AUC, and computational time, using an 80:20 stratified split and 5-fold cross-validation. Random Forest achieved the best overall performance (accuracy: 0.9163, F1-score: 0.9133, ROC-AUC: 0.9784), while tuned SVM (C=10, RBF kernel) attained the highest accuracy of 0.9473 and F1-score of 0.9321. Cross-validation on Naive Bayes showed consistent results with an average accuracy of 88.09%. Further analysis using Logistic Regression coefficients identified influential features: positive sentiment associated with words like "mantap", "mudah", and "sukses", while negative sentiment correlated with "kecewa", "parah", and "lemot". These insights provide practical value for Tokopedia's teams to enhance user experience, such as improving app speed and addressing complaints. The findings demonstrate the effectiveness and efficiency of traditional machine learning techniques for sentiment analysis in Bahasa Indonesia contexts.
Public Sentiment Analysis on Corruption Issues in Indonesia Using IndoBERT Fine-Tuning, Logistic Regression, and Linear SVM Kono, Maria Fatima; Fajri, Ika Nur; Pristyanto, Yoga
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10537

Abstract

Sentiment analysis is a method in Natural Language Processing (NLP) that aims to understand public perceptions based on textual data from social media. Opinions expressed in digital platforms play an important role as they reflect public trust and attitudes toward strategic issues in Indonesia. This study aims to compare the performance of three IndoBERT-based approaches for sentiment classification, namely IndoBERT with full fine-tuning, IndoBERT as a feature extractor combined with Logistic Regression, and IndoBERT as a feature extractor combined with Linear SVM. The dataset was collected through the Twitter API, consisting of 2,012 tweets, which after preprocessing and balancing resulted in 2,252 labeled data for positive and negative sentiments. The preprocessing stage included cleansing, normalization, tokenization, stopword removal, and stemming. The dataset was then split into 80% training data, 10% validation data, and 10% testing data. Experimental results show that IndoBERT with full fine-tuning achieved the best performance, with an accuracy of 82.67%, an F1-score of 82.35%, and an AUC value of 0.87. Logistic Regression and Linear SVM produced lower accuracies of 80.20% and 78.22%, respectively. These findings indicate that fine-tuned IndoBERT is more effective in capturing the semantic nuances of the Indonesian language, while the non fine-tuning approaches offer better computational efficiency at the cost of reduced accuracy. This study contributes to the development of NLP methods for the Indonesian language, particularly in sentiment analysis, and highlights the potential of transformer-based models for analyzing strategic issues in social media.
Co-Authors Aditya Salman Agung Nugroho Andi Muh. Rahul Rajes Topares Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Ardani, Lutfasari arif nur rohman Asti Astuti, Ika ATIK NURMASANI Ayurira, Caren Legisna Aqila Barus, Herianta Bety Wulan Sari, Bety Wulan Daniel, Gilberth Patrick Dari, Aprillia Wulan Nanda Darmayanti, Elda Putri Dendi Agung Muhaziz Dismas Banar Purnandi Donni Prabowo Dwi Hartanto, Anggit Dyah Anggita, Sharazita Eli Pujastuti, Eli Etik Anjar Fitriarti, Etik Anjar Femi Dwi Astuti hallan, rosalia roja Hanifan, Hafid Hayaty, Mardhiya Hendra Kurniawan Hendra Kurniawan Hijriah, Az Zahra Irwanto, Bagas Kono, Maria Fatima Kurniawan, Febri Dwi Mahfud, Arisman Mangli, Luh Ajeng Roro Muhammad Farhan Muhammad Irvan Muis, Nasrul Amin Murtiningsih, Dewi Ayu Mu’alif Lihawa Norhikmah Norhikmah Nugroho, Agung Nur Indah Kusumawardhani Nurhalisa, Vitra Pangestu, Rafel Alansyah Pinasti, Rafa Hadiya Pratama, Akbar Pratama, Kelvin Jaya Pratama, Subhan Rizky Primanda, Aldyan Gilang Prisyanto, Yoga Putri Anggara, Rindina Adisya Radhita Rayhan Rahman Saputra, Rahman Rana Aphrodita, Ishiqa Rayhan, Radhita Rohim, Dwi Nur Roy Wenang Robbani Setioadi, Rizkiansyah Eka Sifa’ul Husna, Siti Okta Siska Siska Syamsul A Syahdan, Syamsul A W, Bambang Soedijono Widodo, Tegar Robi Wiwi Widayani Yoga Pristyanto Zahrotus Sa'idah Zaidan Putra, Bazil