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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi dan Sistem Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Abdimas Indonesia Journal Of Artificial Intelligence And Software Engineering Journal of Computer Science and Information Technology Masyarakat Mandiri: Jurnal Pengabdian Dan Pembangunan Lokal Jurnal Sistem Informasi dan Teknologi Informasi
Andrianingsih
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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DAMPAK KESIAPAN ORGANISASI TERHADAP KEBERHASILAN CLOUD COMPUTING DI UMKM DENGAN MODEL DELONE DAN MCLEAN Sani, Asrul; Andrianingsih, Andrianingsih; Aisyah, Siti; Taufik, Ahmad
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.325

Abstract

This study investigates the critical success factors for cloud computing adoption in small and medium enterprises (SMEs) in Jabodetabek by integrating the DeLone and McLean information systems success model with organizational readiness. Through a quantitative survey approach, the research examines how system quality, information quality, and service quality influence organizational readiness and, subsequently, user satisfaction and net benefits of cloud computing. Results indicate that service quality has the strongest impact on organizational readiness, underscoring the importance of technical support in facilitating technological change. Furthermore, organizational readiness significantly enhances user satisfaction and operational benefits, emphasizing that well-prepared internal structures and skilled human resources are essential for successful cloud adoption. The study provides valuable insights for SME managers to strengthen organizational readiness, particularly in cultural and technical aspects, to optimize cloud computing outcomes. It also offers practical recommendations for policymakers to design training programs that enhance SMEs' technological capabilities. This research contributes to the literature by integrating quality and readiness perspectives, filling a gap in understanding how these factors jointly influence cloud adoption in SMEs. Future research could explore the long-term impacts of cloud adoption and validate these findings across different sectors and regions. ABSTRAKPenelitian ini mengkaji faktor-faktor keberhasilan kritis dalam adopsi cloud computing pada usaha mikro, kecil, dan menengah (UMKM) di wilayah Jabodetabek dengan mengintegrasikan model kesuksesan sistem informasi DeLone dan McLean serta kesiapan organisasi. Melalui pendekatan survei kuantitatif, penelitian ini menganalisis bagaimana kualitas sistem, kualitas informasi, dan kualitas layanan memengaruhi kesiapan organisasi dan selanjutnya kepuasan pengguna serta manfaat bersih dari cloud computing. Hasil penelitian menunjukkan bahwa kualitas layanan memiliki dampak terkuat pada kesiapan organisasi, menyoroti pentingnya dukungan teknis dalam memfasilitasi perubahan teknologi. Selain itu, kesiapan organisasi secara signifikan meningkatkan kepuasan pengguna dan manfaat operasional, menegaskan bahwa struktur internal yang siap dan sumber daya manusia yang terampil sangat penting untuk keberhasilan adopsi cloud. Studi ini memberikan wawasan praktis bagi manajer UMKM untuk memperkuat kesiapan organisasi, terutama dalam aspek budaya dan teknis, guna mengoptimalkan hasil adopsi cloud computing. Rekomendasi juga diberikan kepada pembuat kebijakan untuk merancang program pelatihan yang meningkatkan kapabilitas teknologi UMKM. Penelitian ini berkontribusi pada literatur dengan menggabungkan perspektif kualitas dan kesiapan, mengisi kesenjangan dalam memahami pengaruh bersama faktor-faktor ini terhadap adopsi cloud pada UMKM. Penelitian selanjutnya dapat mengeksplorasi dampak jangka panjang adopsi cloud dan memvalidasi temuan ini di sektor dan wilayah berbeda.
Perancangan User Experience Aplikasi Lapor Vaksin Kelurahan Menggunakan Metode UCD (User Centered Design) Putra, Gifari Yudiya; Andrianingsih, Andrianingsih; Aldisa, Rima Tamara
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.457

Abstract

Urban village is an administrative division of the area under the auspices of the sub-district formed by the sub-district government to assist the implementation of the government in development and society. With the current Covid situation, the kelurahan has an important role to help the government accelerate to implement the program evenly. The problem faced by the community is the lack of information on where and when there is a vaccination schedule. Therefore, in order applications that provide information about vaccines. Based on this problem, the authors conducted testing on 302 respondents using the User Centered Design method and the application of the usability method by prioritizing three aspects with the results, namely learmability, efficiency, and staisfacation. User success rate is 91.10%, time-based efficiency is 0.061794869 and system usability scale is 61.42. the existence of this vaccine report application was built to make it easier for sub-districts to record who has not implemented the vaccine and provide notifications about the nearest mass vaccination program
Comparative Analysis of KNN and Neavy Bayes Algorithms in Socio-Economic Data Classification in Indonesia Buhori, Kiki; Andrianingsih
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.23337

Abstract

The global economy continues to recover as trade flows, employment, and incomes improve. However, the economic recovery is uneven across countries and business sectors. The economic recovery has also resulted in structural changes, meaning that some sectors, jobs, technologies and behaviors will not return to pre-pandemic trends. Future developments depend on local economic conditions. The economy has the most important aspect in a country where the economy makes a country capable of meeting its needs by utilizing limited resources. This study aims to compare two data mining classification algorithms, namely Naïve Bayes and K-Nearest Neighbor, in analyzing socio-economic data in Indonesia. Based on this problem, the data mining classification method is used in determining the algorithm that is suitable for predicting socio-economic data in Indonesia. The two algorithms used are K-NN and Naive Bayes. After testing the two algorithms using confusion matrix and K-Fold Cross Validation, the results obtained from the two models have an accuracy of Naïve Bayes 98.25% and K-NN 97.78% and the results of K-Fold Cross Validation Naïve Bayes 98% and K-NN 96%. Naïve Bayes is superior to K-NN in this context of socioeconomic data classification in Indonesia, especially in terms of accuracy. Although K-NN shows good consistency, Naïve Bayes provides more accurate results.
Spatial Analysis of Random Forest Classification Model for Availability Mapping of Sports Facilities in Jakarta Candra, Hansen; Andrianingsih, Andrianingsih
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6556

Abstract

This research analyzes the distribution of sports facilities in DKI Jakarta Province using spatial modeling and Machine Learning Random Forest algorithm in order to support Indonesia Emas 2045. The goal is to classify areas based on the level of availability of sports facilities into low, sufficient, and high categories, and evaluate the accuracy of the Random Forest algorithm in the classification. CRISP-DM methodology is used in this research, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data analyzed includes spatial sub-district areas and attributes of sports facilities in DKI Jakarta. Random Forest was chosen because of its ability to classify complex data and identify feature importance. The results show that the distribution of sports facilities is uneven, with low categories more in Central Jakarta and North Jakarta, while high categories are scattered in other areas. Random Forest accuracy reached 89%, with high precision and recall in the high category.
Rainfall Classification Based on El-Niño and La-Niña Climate Phenomenon Using Naive Bayes Classifier Algorithm Erlinda, Mely; Andrianingsih, Andrianingsih
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6552

Abstract

As a tropical country, Indonesia faces significant challenges due to global climate phenomena such as El Niño and La Niña that impact rainfall patterns. This research aims to classify daily rainfall in major Indonesian cities such as, DKI Jakarta, Surabaya, Medan, Makassar, and Bandung, into three main categories, namely moderate rain, extreme rain, and no rain. In addition, it identifies climate conditions based on El Niño, La Niña, and Normal categories by applying the Naïve Bayes Classifier algorithm. In this study, the CRISP-DM (Cross-Industry Standard Process for Data Mining) method was used as a framework for processing daily rainfall data for the period January to December 2023, obtained from BMKG. The analysis results show that the Naïve Bayes Classifier algorithm has high performance with 93.15% accuracy, 98% precision, 93% recall, and 94% F1-score. Further analysis, this study found that El Niño causes a significant decrease in rainfall, while La Niña increases extreme rainfall, especially in Makassar and Medan. This research contributes to the development of rainfall classification models that can help the government to anticipate the impacts of climate change and improve the efficiency of water resources management in urban areas.
Analisis Sentimen Aplikasi LinkAja di Google Play Store Menggunakan Algoritma Nave Bayes dan Random Forest Kaeren, Kaeren; Andrianingsih, Andrianingsih
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol 6, No 02 (2025): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v6i02.13821

Abstract

Dompet digital telah menjadi bagian penting dalam transaksi keuangan modern, dengan LinkAja sebagai salah satu penyedia layanan di Indonesia. Meskipun termasuk lima besar dompet digital populer, LinkAja memiliki jumlah ulasan tinggi di Google Play Store, tetapi proporsi pengguna dan rata-rata ratingnya lebih rendah dibandingkan dengan pesaingnya. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna terhadap aplikasi LinkAja dengan mengklasifikasikan opini mereka ke dalam kategori positif dan negatif menggunakan algoritma Nave Bayes dan Random Forest. Hasil penelitian menunjukkan bahwa mayoritas ulasan pengguna bersentimen negatif, dengan permasalahan utama meliputi kesulitan login, kendala transaksi, proses upgrade lambat, serta layanan pelanggan yang kurang responsif. Hal ini menyebabkan rating aplikasi LinkAja didominasi oleh skala terendah, yaitu 1. Banyaknya pengalaman kurang menyenangkan mendorong pengguna untuk aktif memberikan ulasan, yang sebagian besar berisi keluhan terhadap layanan aplikasi. Dalam proses klasifikasi, Random Forest menunjukkan performa lebih unggul dengan akurasi sebesar 82%, presisi 84%, recall 81%, dan f1-score 82%. Sedangkan Nave Bayes memiliki nilai akurasi sebesar 79%, presisi 83%, recall 78%, dan f1-score 79%. Random Forest juga lebih efektif dalam mengidentifikasi sentimen negatif dibandingkan Nave Bayes. Analisis lebih lanjut mengungkapkan bahwa fitur aplikasi dan kemudahan penggunaan menjadi faktor utama dalam ulasan positif, meskipun jumlahnya lebih sedikit dibandingkan ulasan negatif.
ANALISIS SENTIMEN PELANGGAN TERHADAP APLIKASI WONDR BY BNI MENGGUNAKAN NAIVE BAYES, SUPPORT VECTOR MACHINE (SVM), DAN K-NEAREST NEIGHBOR (KNN) Irzan Busrayan; Andrianingsih, Andrianingsih
Journal of Computer Science and Information Technology Vol. 2 No. 2 (2025): Maret
Publisher : Yayasan Nuraini Ibrahim Mandiri

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

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi Wondr by BNI berdasarkan ulasan yang diperoleh dari Google Play Store. Pengumpulan data dilakukan melalui teknik web scraping menggunakan library google-play-scraper dengan parameter tertentu untuk memastikan relevansi dan kesesuaian data dalam konteks lokal. Data yang diperoleh kemudian diproses melalui tahapan preprocessing, yang mencakup case folding, cleansing, normalisasi, tokenisasi, dan penghapusan stopwords guna meningkatkan kualitas analisis. Selanjutnya, data direpresentasikan menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) untuk mengukur bobot kata dalam ulasan. Analisis sentimen dilakukan dengan menerapkan tiga algoritma pembelajaran mesin, yaitu Naïve Bayes, Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN), guna mengklasifikasikan ulasan ke dalam kategori sentimen positif, negatif, dan netral. Hasil penelitian menunjukkan bahwa metode preprocessing yang diterapkan mampu meningkatkan kualitas data untuk analisis sentimen. Algoritma SVM memberikan akurasi tertinggi dibandingkan dengan Naïve Bayes dan KNN dalam mengklasifikasikan sentimen pengguna. Temuan ini dapat dimanfaatkan oleh pengembang aplikasi dalam meningkatkan layanan dan pengalaman pengguna berdasarkan umpan balik yang telah dianalisis. Kata Kunci: Analisis Sentimen, Machine Learning, TF-IDF, Wondr By BNI, Google Play Store
KLASTERISASI DATA PENYAKIT JANTUNG MENGGUNAKAN K-MEANS DALAM SISTEM INFORMASI KESEHATAN Adinda, Saskia; Andrianingsih, Andrianingsih
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.v2i3.2021

Abstract

Penyakit jantung merupakan salah satu isu kesehatan utama yang memerlukan perhatian serius dalam pengelolaannya, terutama dalam menentukan skala prioritas penanganan pasien. Penelitian ini bertujuan untuk menerapkan metode K-Means Clustering dalam analisis data pasien penyakit jantung menggunakan Heart Disease UCI Dataset dari Kaggle, yang mencakup berbagai atribut medis seperti usia, jenis kelamin, tekanan darah, kadar kolesterol, dan jumlah pembuluh darah yang terdeteksi. Tahapan penelitian mencakup pembersihan data, normalisasi, seleksi fitur, serta penerapan algoritma K-Means untuk mengelompokkan pasien berdasarkan kemiripan karakteristik medis mereka. Hasil klasterisasi ini bertujuan untuk membantu tenaga medis dalam menentukan prioritas penanganan pasien berdasarkan tingkat risiko penyakit jantung. Evaluasi menunjukkan bahwa metode K-Means Clustering mampu mengelompokkan pasien dengan baik dan dapat diintegrasikan ke dalam sistem informasi kesehatan rumah sakit untuk meningkatkan efisiensi pengelolaan data serta mempercepat pengambilan keputusan medis. Dengan demikian, penelitian ini berkontribusi dalam pemanfaatan teknologi analitik guna meningkatkan kualitas layanan kesehatan dalam menangani pasien penyakit jantung.
OPTIMALISASI PRIME TIME IKLAN RADIO DENGAN ALGORITMA C4.5 DALAM DATA MINING Sakti Ade Prastyo, Anggoro Bimma; Andrianingsih, Andrianingsih
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.v2i3.2038

Abstract

Penelitian ini bertujuan untuk mengoptimalkan waktu prime time dalam pemasangan iklan radio menggunakan algoritma C4.5 dalam teknik data mining. Dengan meningkatnya persaingan media digital, radio perlu mengembangkan strategi pemasangan iklan yang lebih efektif. Metode yang digunakan melibatkan pengumpulan dan analisis data pendengar radio untuk menentukan pola prime time yang optimal. Hasil penelitian menunjukkan bahwa waktu 06:00 - 09:00 pagi merupakan slot waktu yang paling efektif berdasarkan pola perilaku pendengar. Dengan pemanfaatan algoritma C4.5, penelitian ini berhasil meningkatkan efektivitas pemasangan iklan dan memberikan rekomendasi strategis bagi pengiklan dan stasiun radio.
Capacity-Building Training on Website Development for SMA 58 Jakarta Students Using HTML and JavaScript Programming Languages Fauziah, Fauziah; Ningsih, Sari; Hayati, Nur; Andrianingsih, Andrianingsih; Shalihati, Ira Diana; Riyantoro Riyantoro; Indra Lukmana; Syaiful Syaiful; Muhammad Nurdin; Wijanarko, Sigit
Masyarakat Mandiri : Jurnal Pengabdian dan Pembangunan Lokal Vol. 2 No. 4 (2025): Oktober: Masyarakat Mandiri : Jurnal Pengabdian dan Pembangunan Lokal
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/masyarakatmandiri.v2i4.2140

Abstract

Hyper Text Markup Language (HTML) is a markup language used to structure and format text documents through the use of tags that control the appearance of content on web pages. HTML defines various document elements such as headings, paragraphs, images, and lists, which form the basic structure of a webpage. JavaScript, on the other hand, serves as a programming language that adds interactivity and dynamics to web pages. While HTML provides the basic framework for website layout, JavaScript enhances functionality with features that cannot be achieved through HTML alone, such as dynamically manipulating elements and responding to user interactions. Together, these two languages form the core foundation for web development. This program aims to equip students of SMAN 58 with the skills to practice using HTML tags, understand the basics of JavaScript programming, and develop interactive web pages through online simulations. The training will be conducted in stages, beginning with the submission of a proposal to the school and followed by the scheduled training in June 2024. Students will use the One Compiler platform for real-time practice, allowing them to write and execute HTML and JavaScript code directly in the browser. The training will be conducted interactively, introducing the fundamental concepts of HTML and JavaScript, along with demonstrations showing how to create dynamic websites. In the final phase, students will take a quiz designed to assess their understanding of the material and their ability to apply the knowledge gained. Through this program, it is expected that students will master the basic skills of website creation and have the ability to independently develop their own interactive web projects. Additionally, this program aims to enhance students' digital awareness and technical skills in the era of information technology.
Co-Authors Adinda, Saskia Agung Triayudi Ahmad Taufik Aldisa, Rima Tamara Aldjawad, Mohamad Alhadi, Alhadi Alhakim, Rahmat Ambi Muhammad Dzuhri, Ambi Muhammad Annisa Annisa Asrul Sani Buhori, Kiki Burhani, Hanif Rahman Candra, Hansen Chafidin, Afiyan Nur Dhieka Avrilia Lantana Elok Ainur Latif Endah Tri Esti Handayani Erlinda, Mely Erliza Hambali Fakrurrozi, Fakrurrozi Fauziah Fauziah Firzatullah, Raden Muhamad Hindarto, Djarot Ina Agustina Indra Lukmana Irzan Busrayan Iskandar Fitri, Iskandar Kaeren, Kaeren Kusuma, Rega Andhika Maharani, Anggita Putri Mochamad Hariadi Muhammad Nurdin Muhammad Taufiq Muhammad, Taufik Mutia Pamikatsih Nur Hayati Pratama, Wildan Ikbar Putra, Gifari Yudiya Ramadhan, Jonathan Aditya Ratih Titi Komalasari Riyantoro Riyantoro Sakti Ade Prastyo, Anggoro Bimma Sari Ningsih Septi Andryana setiawan, Danis Shalihati, Ira Diana Siti Aisyah Soepriyono, Gatot Subkhi, Akhmad Yunus Suparman, Kodim Susanto, Arman Rama Syaiful Syaiful Wahyuddin, M Iwan Wahyuddin, Mohammad Iwan WIJANARKO, SIGIT Wiratama Putra, Tobby Zuraidah, Arika