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All Journal Jurnal Ilmiah Informatika Komputer JUITA : Jurnal Informatika 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.
Klasifikasi Penyakit Anemia Menggunakan Algoritma Navïe Bayes Elda Putri Darmayanti; Ika Nur Fajri
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i1.94743

Abstract

Abstrak:Anemia merupakan kondisi medis yang umum di mana darah seseorang kekurangan sel darah merah yang sehat atau hemoglobin. Hemoglobin adalah protein dalam sel darah merah yang berfungsi untuk mengangkut oksigen dari paru-paru ke seluruh tubuh ketika seseorang terkena anemia, mereka mungkin merasa lelah, lemah, dan sesak napas. Anemia dapat disebabkan oleh berbagai faktor, termasuk kekurangan zat besi, vitamin B12, atau folat; kehilangan darah; dan kerusakan sumsum tulang. Dalam upaya untuk meningkatkan diagnosis awal dan akurasi klasifikasi penyakit anemia, penelitian ini menerapkan algoritma Naïve Bayes. Dataset yang digunakan dalam penelitian ini adalah dataset penyakit anemia yang didapatkan dari website kaggle.com, yang mencakup atribut-atribut penting seperti Gender, Hemoglobin, MCH, MCHC, MCV, dan Result. Pemilihan Naïve Bayes sebagai salah satu algoritma yang diuji didasarkan pada keunggulannya dalam menangani data dengan atribut sederhana serta kemampuannya mengelola data yang mengandung ketidakpastian. Naïve Bayes dikenal sebagai algoritma yang efisien untuk pengolahan dataset berukuran besar dengan struktur data yang sederhana. Selain itu, algoritma ini sering menjadi pilihan pada tahap awal eksplorasi data karena kesederhanaan implementasi, kecepatan pemrosesan, dan kemampuannya menghasilkan hasil yang cukup akurat dalam berbagai kondisi. Meskipun Naïve Bayes mungkin tidak selalu lebih akurat daripada SVM atau Decision Tree dalam kasus kompleks, algoritma ini menawarkan solusi yang lebih cepat, ringan, dan mudah diimplementasikan, yang sangat relevan untuk aplikasi medis dengan sumber daya terbatas. Pemilihan Naïve Bayes dalam penelitian ini bertujuan untuk mengeksplorasi keseimbangan antara kecepatan, efisiensi, dan akurasi dalam klasifikasi penyakit anemia=======================================Abstract:Anaemia is a common medical condition where a person's blood lacks healthy red blood cells or haemoglobin. Haemoglobin is a protein in red blood cells that serves to transport oxygen from the lungs to the rest of the body. When a person is anaemic, they may feel tired, weak, and short of breath. Anaemia can be caused by various factors, including iron, vitamin B12, or folate deficiency; blood loss; and bone marrow damage. In an effort to improve the early diagnosis and classification accuracy of anaemia, this study applied the Naïve Bayes algorithm. The dataset used in this research is an anaemia disease dataset obtained from the website kaggle.com, which includes important attributes such as Gender, Haemoglobin, MCH, MCHC, MCV, and Result. The selection of Naïve Bayes as one of the tested algorithms is based on its superiority in handling data with simple attributes and its ability to manage data containing uncertainty. Naïve Bayes is known as an efficient algorithm for processing large datasets with simple data structures. Moreover, it is often the algorithm of choice in the early stages of data exploration due to its simplicity of implementation, processing speed, and ability to produce reasonably accurate results under various conditions. While Naïve Bayes may not always be more accurate than SVM or Decision Tree in complex cases, it does offer a bargain
Perancangan Sistem Informasi Pemesanan Berbasis Web di Restoran Pawon Jinawi Aldyan Gilang Primanda; Ika Nur Fajri
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i1.93524

Abstract

Abstrak:Pawon Jinawi merupakan sebuah usaha restoran yang bergerak dalam bidang penjualan makanan dan minuman. Restoran ini menyajikan berbagai menu rumahan khas Jawa. Namun, sistem pemesanan dan pengelolaan data di restoran ini masih dilakukan secara manual, yang menimbulkan masalah seperti lambatnya pelayanan dan risiko kehilangan catatan pesanan. Penelitian ini bertujuan untuk mengembangkan sistem informasi berbasis web yang memudahkan pelanggan dalam melakukan pemesanan  secara online. Sistem ini juga dirancang untuk membantu karyawan dalam pengelolaan data pesanan dan pembayaran secara lebih terstruktur. Metode pengembangan yang digunakan adalah waterfall, sebuah metode pengembangan perangkat lunak yang berjalan secara berurutan dan sistematis. Setiap tahap diselesaikan secara menyeluruh sebelum melanjutkan ke tahap berikutnya, untuk tahapannya mencakup lima tahap Requirement, Design, Implementation, Verification, dan Maintenance. Pengujian sistem dilakukan menggunakan metode Black box. Hasil penelitian ini yaitu sebuah sistem informasi pemesanan berbasis web. Setelah implementasi sistem akan meningkatkan efisiensi operasional restoran, dengan waktu pemrosesan pesanan yang berkurang sebesar 45%, serta peningkatan kepuasan pelanggan dalam hal kecepatan dan kemudahan proses pemesanan. Penelitian ini didukung oleh beberapa penelitian terdahulu yang menunjukkan manfaat dari penerapan sistem informasi untuk pengelolaan pemesanan di restoran==================================================Abstract:Pawon Jinawi is a restaurant business engaged in the sale of food and beverages. This restaurant serves a variety of Javanese home-style menus. However, the ordering system and data management in this restaurant are still done manually, which causes problems such as slow service and the risk of losing order records. This study aims to develop a web-based information system that makes it easier for customers to place orders online. This system is also designed to help employees manage order and payment data in a more structured way. The development method used is waterfall, a software development method that runs sequentially and systematically. Each stage is completed thoroughly before proceeding to the next stage, for the stages include five stages of Requirement, Design, Implementation, Verification, and Maintenance. System testing is carried out using the Black box method. The results of this study are a web-based ordering information system. After the implementation of the system will increase the operational efficiency of the restaurant, with order processing time reduced by 45%, as well as increased customer satisfaction in terms of speed and ease of the ordering process. This research is supported by several previous studies that show the benefits of implementing an information system for managing orders in restaurants
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.
Co-Authors Aditya Salman Aldyan Gilang Primanda 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 Dendi Agung Muhaziz Dismas Banar Purnandi Donni Prabowo Dwi Hartanto, Anggit Dyah Anggita, Sharazita Elda Putri Darmayanti 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 Irvan 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 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