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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknologi dan Rekayasa SITEKIN: Jurnal Sains, Teknologi dan Industri Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science (JOINTECS) Jurnal Pilar Nusa Mandiri J I M P - Jurnal Informatika Merdeka Pasuruan JSiI (Jurnal Sistem Informasi) Ensiklopedia of Journal Minda Baharu Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal PkM (Pengabdian kepada Masyarakat) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Innovative: Journal Of Social Science Research Journal of Research and Publication Innovation SAGA: Journal of Technology and Information Systems SWADIMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT Journal of Computer Science and Information Technology
Endah Tri Esti Handayani
Universitas Nasional
Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Revolution in Image Data Collection: CycleGAN as a Dataset Generator Hindarto, Djarot; Handayani, Endah Tri Esti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13211

Abstract

Computer vision, deep learning, and pattern recognition are just a few fields where image data collection has become crucial. The Cycle Generative Adversarial Network has become one of the most effective instruments in the recent revolution in image data collection. This research aims to comprehend the impact of CycleGAN on the collection of image datasets. CycleGAN, a variant of the Generative Adversarial Network model, has enabled the unprecedented generation of image datasets. CycleGAN can transform images from one domain to another without manual annotation by employing adversarial learning between the generator and discriminator. This means generating image datasets quickly and efficiently for various purposes, from object recognition to data augmentation. One of the most fascinating features of CycleGAN is its capacity to alter an image's style and characteristics. Using CycleGAN to generate unique and diverse datasets assists deep learning models in overcoming visual style differences. This is a significant development in understanding how machine learning models can comprehend visual art concepts. CycleGAN's use as a data set generator has altered the landscape of image data collection. CycleGAN has opened new doors in technological innovation and data science with its proficiency in generating diverse and unique datasets. This research will investigate in greater detail how CycleGAN revolutionized the collection of image datasets and inspired previously unconceived applications.
Perbandingan Kinerja Algoritma K-Nearest Neighbors (K-NN) Dan Decision Tree dalam Deteksi Paket Malis pada Jaringan Kasmara, Bib Nugraha; Handayani, Endah Tri Esti; Nathasia, Novi Dian
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 8, No 3 (2024)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v8i3.22362

Abstract

This research aims to classify malicious packet data and compare the performance of two algorithms, namely K-Nearest Neighbor (K-NN) and Decision Tree (DT). The UNSW-NB15 dataset used in this study has undergone preprocessing, feature selection, and data split stages. The preprocessing stage includes data transformation and selection of relevant features to detect malicious packets. Subsequently, experiments were conducted to test various values of K in K-NN and measure accuracy, recall, precision, and F1-Score. The results show that K-NN has an accuracy of 91.54%, while DT has 92.41%. The conclusion of this research indicates that the Decision Tree (DT) algorithm performs slightly better than K-Nearest Neighbor (K-NN) in detecting malicious packets. Therefore, in selecting an algorithm for network security detection, it is important to consider the specific needs and goals of the research as well as the characteristics of the data used.
Application of Academic Potential Test for New Student Admission Using Fisher-Yates Shuffle Algorithm Abdul Azis; Agung Triayudi; Endah Tri Esthi Handayani
SAGA: Journal of Technology and Information System Vol. 2 No. 1 (2024): February 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/saga.v2i1.254

Abstract

The selection process of new students in educational institutions, such as in Sekolah Menengah Kejuruan (SMK) Wisata Indonesia which still uses conventional methods using paper, has several challenges. One of the main challenges is test cheating, where prospective students may try to manipulate their test results to increase their chances of being accepted. In addition, another challenge faced by SMK Wisata Indonesia is that when the number of prospective students who register is very large, managing answers and exam results can become more complicated. This research aims to design and build an Android-based Academic Potential Test application by applying the Fisher-Yates Shuffle algorithm to randomize the order of questions. This research also uses the RUP (rational Unified Process) system development technique which has several phases, namely the Inception phase, Elaboration phase, Construction phase and Transition phase. The validation testing carried out obtained overall valid results so that the application that has been designed is in accordance with user needs. Meanwhile, usability testing in the Academic Potential Test Application using the SEQ method resulted in an average Likert score for students of 6.63 with a user-friendliness percentage of around 94%. As for teachers, the Likert average score is 6.53 with a percentage of ease of use of around 93%. This shows that the Academic Potential Test Application that has been built is EASY TO USE
IMPLEMENTASI ALGORITMA HAVERSINE UNTUK PERHITUNGAN JARAK ANTARA LOKASI PERUSAHAAN DENGAN KARYAWAN PADA PT MEGA GIGA SOLUSIND Luthfia Nur Aini; Endah Tri Esti Handayani; Rini Nuraini
Journal of Research and Publication Innovation Vol 2 No 4 (2024): OCTOBER
Publisher : Journal of Research and Publication Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Luthfia Nur Aini, 227064426164, Informatics Study Program. The use of mobile devices has significantly expanded in Indonesia, including in employee attendance systems. However, many companies still rely on manual attendance methods, such as fingerprint systems, which present several drawbacks, particularly in supporting remote work and operational efficiency. This study aims to develop a mobile attendance application using the Haversine algorithm to calculate the distance between employees' locations and the office at PT Mega Giga Solusindo. The Haversine algorithm is employed to ensure accurate distance calculation based on geographical coordinates, facilitating automated and real-time attendance tracking. This application is expected to address the challenges of manual attendance and enhance the company's operational efficiency. The research methodology involves software development using the Agile approach, and the application is tested through blackbox testing to ensure its functionality.
CLASSIFICATION OF HEART DISEASE USING THE K-NEAREST NEIGHBOR ALGORITHM AND LOGISTIC REGRESSION Sugitha, I Kadek Agga; Triayudi, Agung; Handayani, Endah Tri Esti
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5742

Abstract

Heart disease is a major cause of death in the world, including in Indonesia, with increasing rates and death rates that carry a huge burden on health and society. Lack of awareness of early signs contributes significantly to this challenge. This study aims to prevent heart disease through early diagnosis using K-Nearest Neighbor (K-NN) and Logistic Regression algorithms. The database, obtained from Kaggle.com, includes 15 clinical units for cardiac diagnosis. The test shows that the K-NN method with k = 3 achieves the highest performance on the experimental data (30%), with 90% precision, 93% precision, 87% recall, and 90% f1 - score. In comparison, Logistic Regression and sigmoid achieved 86% precision, 83% precision, 90% recall, and 86% f1-score on the same experimental data. These results show that K-Nearest Neighbor is better than Logistic Regression as a classification algorithm for heart disease database. Applying these findings to the web-based Streamlit system is expected to improve the efficiency and timeliness of heart disease screening.
PENINGKATAN PENJUALAN UMKM ALBY KEY DENGAN PEMASARAN DIGITAL Mardiani, Eri; Rahmansyah, Nur; Ningsih, Sari; Handayani, Endah Tri Esti; Hidayatullah, Deny; Desmana, Satriawan; Lantana, Dhieka Avrilia; Fachry, Fachry; Suhatmojo, Guing Tri; Nurfaiz, Kelfin; Perdana, Muhammad Rizky; Putro, Prayogo Dwi Cahyo; Dhema, Salestinus Petrus; Prasetyo, Yoga Dwi
MINDA BAHARU Vol 7, No 1 (2023): Minda Baharu
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/jmb.v7i1.5330

Abstract

Pandemi covid-19 sangat berdampak sekali terhadap UMKM serta bagi yang baru membuat wirausaha, dengan kondisi peralihan dari masa pandemi ke endemi, penjualan dengan secara konvensional sangat tidak efektif, agar penjualan dapat berjalan dengan baik, maka pelaku usaha harus dapat mengembangkan usahanya. Untuk bangkit kembali mengembangkan usahanya maka pelaku usaha harus mampu meningkatkan potensi diri menyesuaikan kondisi saat ini sehingga pelaku melakukan wirausaha dengan efisien, salah satu untuk meningkatkan penjualan, pelaku usaha harus mengoptimalkan pemasaran penjualan dengan sistem digital, dengan menggunakan potensi diri dan keinginan pelaku usaha untuk mengembangkan pemasaran maka peningkatan penjualan menggunakan sistem digital jauh lebih mudah untuk mengembangkan usaha. Dengan menggunakan Social Customer Relationship Management (SCRM) untuk membantu end-user memanfaatkan jejaring sosial, data internal dan eksternal, umpan berita, serta konten penjualan dan pemasaran yang ada dengan lebih baik. Contohnya dengan menggunakan e-commerce dan media sosial untuk mempermudah promosi. Karena era digital saat ini, pemasaran produk UMKM menggunakan situs web yang tepat, memiliki manfaat yang sangat besar karena promosi penjualan atau pemasaran dapat menjangkau target konsumen dengan jangkauan yang lebih luas dan dengan jaminan layanan yang optimal dengan biaya yang relatif murah dan lebih efisien. Untuk sukses di era digital, UMKM juga perlu mengelola strategi pemasarannya dengan memanfaatkan teknologi digital.
Enhancing image quality using super-resolution residual network for small, blurry images Hindarto, Djarot; Wahyuddin, Mohammad Iwan; Andrianingsih, Andrianingsih; Komalasari, Ratih Titi; Handayani, Endah Tri Esti; Hariadi, Mochamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4654-4666

Abstract

In the background, when low-resolution images are utilized, image identification tasks are frequently hampered. By employing the residual network super-resolution framework, super-resolution techniques are used to enhance image quality, specifically in the detection and identification of small and blurry objects. Improving resolution, decreasing blur, and enhancing object detail are the main goals of the suggested approach. The novelty of this research resides in its application of the activation exponential linear unit (ELU) to the super-resolution residual network (SR-ResNet) framework, which has been demonstrated to enhance image sharpness. The experimental findings demonstrate a substantial enhancement in the quality of the images, as evidenced by the training data's structural similarity index (SSIM) of 0.9989 and peak signal-to-noise ratio (PSNR) of 91.8455. Furthermore, the validation data demonstrated SSIM 0.9990 and PSNR 92.5520. The results of this study indicate that the implementation of SR-ResNet significantly enhances the capability of the detection system to detect and classify diminutive and opaque entities precisely. The expected and projected enhancement in image quality significantly influences image processing, especially in situations where accuracy and object differentiation are vital.
OPTIMALISASI KARTU STOK PRODUK DENGAN METODE PERPETUAL Muhammad Rival; Endah Tri Esti Handayani; Frenda Farahdina
Journal of Computer Science and Information Technology Vol. 2 No. 3 (2025): Juni
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v2i3.2019

Abstract

Manajemen persediaan yang efisien menjadi faktor krusial dalam meningkatkan produktivitas dan efektivitas operasional suatu perusahaan. Salah satu metode yang dapat diterapkan untuk mengoptimalkan pencatatan stok adalah metode perpetual, yang memungkinkan pencatatan secara real-time dan lebih akurat. Penelitian ini bertujuan untuk menganalisis dampak penerapan metode perpetual dalam optimalisasi kartu stok produk guna meningkatkan efisiensi sistem manajemen persediaan. Metode ini juga berkontribusi pada digitalisasi sistem pergudangan serta mendukung kebijakan ramah lingkungan dengan mengurangi penggunaan kertas.Hasil penelitian menunjukkan bahwa implementasi metode perpetual berdampak positif terhadap pengelolaan stok barang. Dengan adanya pencatatan secara otomatis dan terintegrasi, efisiensi kerja karyawan gudang meningkat, waktu pencatatan dan pelacakan stok menjadi lebih singkat, serta risiko kesalahan dalam pencatatan dapat diminimalisir. Selain itu, sistem ini membantu perusahaan dalam menyusun strategi pengadaan barang secara lebih akurat berdasarkan data real-time, sehingga dapat mengurangi risiko kelebihan atau kekurangan stok. Pembahasan dalam penelitian ini juga mengungkapkan bahwa adopsi metode perpetual mendorong digitalisasi sistem pergudangan, yang sejalan dengan perkembangan teknologi di era industri 4.0. Dengan mengurangi pencatatan manual berbasis kertas, metode ini turut mendukung program keberlanjutan lingkungan serta menghemat biaya operasional perusahaan dalam jangka panjang. Oleh karena itu, penerapan metode perpetual dalam manajemen persediaan tidak hanya meningkatkan efisiensi operasional, tetapi juga memberikan manfaat ekonomi dan lingkungan yang lebih luas.
ANALISIS SENTIMEN KUALITAS APLIKASI DISCORD MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE Dicki Fareza, Ichsan; Tri Esti Handayani, Endah
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13362

Abstract

Awalnya Discord merupakan aplikasi yang sering digunakan oleh para gamer sebagai platform komunikasi, namun seiring berjalannya waktu, Discord telah memperluas basis penggunanya untuk mencakup beragam komunitas besar. Jumlah pengguna Discord yang kian bertambah, sangat dipengaruhi oleh persepsi dan ulasan positif dari para pengguna. Namun, tidak dapat dipungkiri bahwa masih ada ulasan negatif yang menunjukkan adanya aspek-aspek tertentu yang perlu diperbaiki. Sebagian besar ulasan pengguna Discord di Google Play Store memiliki rating di bawah 5 bintang dan banyak pengguna mengeluhkan adanya bug atau kendala dalam aplikasi. Penelitian ini bertujuan untuk mengetahui tingkat kepuasan pengguna terhadap aplikasi Discord, serta memberikan masukan atau support kepada pengembang Discord mengenai aspek aplikasi yang perlu ditingkatkan atau perlu diperbaiki. Dengan menggunakan algoritma Naïve Bayes dan Support Vector Machine untuk melakukan klasifikasi pada analisis sentimen ulasan pengguna aplikasi Discord, kita dapat memahami lebih jauh bagaimana persepsi tersebut memengaruhi kualitas Discord. Hasil penelitian menunjukkan bahwa model SVM memiliki kinerja yang lebih baik dibandingkan Naive Bayes, dengan nilai akurasi keseluruhan mencapai 88%. Sementara itu Naive Bayes memperoleh akurasi keseluruhan sebesar 78%.
Co-Authors Abdul Azis Adi Firman Ari Saputra Adi Yulianto Aditya Nur Rohman Agung Triayudi Ahmad Rifqi Alica Dwi Fahira Andrianingsih Anggira Ganda Kusuma Arie Gunawan Astri Pertiwi Atikah Suhaimah Azzaleya Agashi Lombu Bagos Fitrianto Wibowo Cintia Marito Sihombing Darussalam, Ucuk Daud Iswandii Dendy Virgiawan Deny Hidayatullah Deny Hidayatullah Deny Hidayatullah Desmana, Satriawan Dhema, Salestinus Petrus Dhieka Avrilia Lantana Dhieka Avrilia Lantana Dicki Fareza, Ichsan Dimas Tri Pamungkas Dwi Ifan Ramadhan Eri Mardiani Eri Mardiani Fachry, Fachry Fardila Inastiana Fauziah Fauziah Febry, Fransiskus Ferina Gunawan Frankly Sept Genius Zendrato Fransiskus Febry Frenda Farahdina Handoko, Suhandio Hindarto, Djarot Imelta Natalia Ginting Inastiana, Fardila Indra Mahendra Iskandar Fitri Iskandar Fitri, Iskandar Kartika Salma Nadhiva Kasmara, Bib Nugraha Keysha Belynda Tyva Panggabean Luthfia Nur Aini Mardiani, Eri Mochamad Hariadi Mohammad Iwan Wahyuddin Muhammad Farhan Adistyra Muhammad Prabowo Chaniago Muhammad Rival Mutiara Mala Khairunnisa Nabila Puspita Wulandana Nabilah Ananda Pratiwi Nathasia, Novi Dian Nur Iskandar Zulkarnaen Nur Rahmansyah Nur Rahmansyah Nurfaiz, Kelfin Oka Saputra Oka Saputra Olipa Sarta Matilda Purba Perdana, Muhammad Rizky Prasetyo, Yoga Dwi Putro, Prayogo Dwi Cahyo Rahmansyah, Nur Ratih Titi Komalasari Ratih Tri Lestari Rini Nuraini Rizky Ramadhan Rizky Ramadhan, Rizky Rosyidah Rahmah Rudi Adityawan Rudi Priyana Sari Ningsih Setiono, Aji Shafira Shalehanny Sisca Budyarti Sugitha, I Kadek Agga Suhaimah, Atikah Suhatmojo, Guing Tri Sultana Namira Teuku Feraldy Ramadhani Trie Widiarti Ningsih Ucuk Darussalam Utami, Yulianti Pratiwi Wahyuddin, Mohammad Iwan Yulianti Pratiwi Utami Yuni Latifah Yusriana Chusna Fadilah