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All Journal Jurnal Ilmu Komputer dan Informasi Prosiding SNATIKA Vol 01 (2011) SITEKIN: Jurnal Sains, Teknologi dan Industri SMATIKA Jurnal Ilmiah KOMPUTASI Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo INTECOMS: Journal of Information Technology and Computer Science JURIKOM (Jurnal Riset Komputer) Minda Baharu STRING (Satuan Tulisan Riset dan Inovasi Teknologi) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Mantik Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal PkM (Pengabdian kepada Masyarakat) JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sistem Informasi Bisnis (JUNSIBI) Green Intelligent Systems and Applications JUNIF: Jurnal Nasional Informatika Abdi Implementasi Pancasila: Jurnal Pengabdian kepada Masyarakat Innovative: Journal Of Social Science Research Antasena: Governance and Innovation Journal SWADIMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT Masyarakat Mandiri: Jurnal Pengabdian Dan Pembangunan Lokal
Sari Ningsih
Fakultas Teknologi Komunikasi Dan Informatika - Universitas Nasional
Agriculture, Biological Sciences & Forestry Arts Humanities 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 Mathematics Mechanical Engineering Nursing Public Health Social Sciences Veterinary Other

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Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV Chuy Mandala Putra; Agung Triayudi; Sari Ningsih
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3532

Abstract

During the COVID-19 pandemic, masks are one of the main measuring tools in carrying out health protocols, masks are also a top priority when carrying out activities outside the home or office. Because masks are quite effective in filtering out disease particles that allow users not to get infected. Therefore, many places have made masks an important requirement in maintaining health protocols during the COVID-19 pandemic. Previously there was a system that had been created to assist the government in implementing the mandatory wearing of masks, but there were still deficiencies. Therefore the authors created a system to detect mask wearing by updating previous researchers using the convolutional neural network (CNN) algorithm. For making this system the author uses the PYTHON and OPENCV programming languages. which will produce four parts in this detection, namely Mask, No Mask, Covered Mouth Chin and Covered Nose Mouth.
Classification of Potential Tsunami Disaster Due to Earthquakes in Indonesia Based on Machine Learning Mardiani, Eri; Rahmansyah, Nur; Ningsih, Sari; Lantana, Dhieka Avrilia; Wulandana, Nabila Puspita; Lombu, Azzaleya Agashi; Budyarti, Sisca
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2084

Abstract

Earthquakes and tsunamis pose significant threats to Indonesia due to its unique geological positioning at the convergence of four tectonic plates. This study focuses on classifying the potential occurrence of tsunami disasters following earthquakes using various data mining methods, including k-Nearest Neighbor (kNN), Naïve Bayes, Decision Tree and Ensemble Method, and Linear Regression. The research employs a qualitative approach to systematically understand and describe the context of natural disasters, utilizing both primary and secondary data collection techniques. Performance evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, and Recall are utilized to assess the effectiveness of each method in predicting potential tsunami events. The findings reveal that the kNN method exhibits the highest performance, with an AUC of 94.4% and a precision of 82.8%, indicating robust predictive capabilities. However, misclassifications were observed, emphasizing the need for further refinement. Naïve Bayes also shows promising results with an AUC of 84.5% and precision of 78.6%. Decision Tree and Ensemble Method models, such as Random Forest and AdaBoost, demonstrate reasonable performance, with Random Forest achieving the highest AUC of 71.9%. Linear Regression is employed to explore the correlation between earthquake attributes and tsunami occurrence, revealing a weak relationship. Further research integrating advanced modeling approaches and additional earthquake attributes is recommended to enhance the predictive capabilities of tsunami risk assessment models. The study underscores the importance of employing diverse machine learning techniques and evaluating their performance metrics to refine the accuracy of tsunami prediction models, ultimately contributing to practical disaster preparedness and mitigation strategies.
ANALISIS HYBRID METODE CNN DAN LSTM DALAM MEDIA BERITA ONLINE INDONESIA Guridno, Ciptoningaji; Azimah, Ariana; Ningsih, Sari
Jurnal Sistem Informasi Bisnis (JUNSIBI) Vol 5 No 1 (2024): Jurnal Sistem Informasi Bisnis (JUNSIBI)
Publisher : Program Studi Sistem Informasi Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55122/junsibi.v5i1.1202

Abstract

Berita palsu atau disinformasi merupakan ancaman serius dalam ekosistem media online. Penyebaran berita palsu dapat mengganggu informasi yang akurat dan dapat mempengaruhi masyarakat dan opini publik. Dalam penelitian ini, Penulis mengusulkan pendekatan hibrida yang mengintegrasikan Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM) untuk menganalisis konten media berita online di Indonesia. Metode hibrid ini ditujukan untuk memahami dan menginterpretasikan dinamika informasi yang disampaikan melalui berita online dengan lebih efektif. Penulis mengumpulkan dan memproses dataset besar dari artikel berita online dalam Bahasa Indonesia, lalu menerapkan CNN untuk ekstraksi fitur teks dan LSTM untuk memodelkan sekuensialitas data dalam artikel. Hasil eksperimen menunjukkan bahwa model hibrid CNN-LSTM mampu meningkatkan akurasi klasifikasi topik berita dan sentiment analisis dibandingkan dengan metode standar. Penelitian ini memberikan wawasan baru tentang aplikasi pembelajaran mesin dalam media berita dan menawarkan metode yang inovatif untuk analisis teks pada skala besar.
IMPLEMENTASI RESTFUL API PADA SISTEM PEMESANAN DAN PENGIRIMAN MAKANAN Adinta, Brema; Winarsih, Winarsih; Ningsih, Sari
Jurnal Sistem Informasi Bisnis (JUNSIBI) Vol 5 No 2 (2024): Jurnal Sistem Informasi Bisnis (JUNSIBI)
Publisher : Program Studi Sistem Informasi Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55122/junsibi.v5i2.1203

Abstract

Penelitian ini bertujuan untuk mengimplementasikan RESTful API pada sistem pemesanan dan pengiriman makanan dengan fokus utama pada peningkatan komunikasi data. Fokus Penelitian adalah pada penggunaan RESTful API sebagai protokol komunikasi dalam konteks pengembangan sistem pemesanan dan pengiriman makanan. Penelitian ini diharapkan dapat memberikan wawasan yang bernilai tentang bagaimana penerapan RESTful API dapat meningkatkan daya saing bisnis di sektor kuliner. Informasi data primer yang mendukung penelitian berasal dari hasil wawancara dengan pemilik Njayo Cafe, serta data sekunder seperti jurnal ilmiah, artikel, dokumen perusahaan terkait, serta publikasi yang membahas teknologi RESTful API. Penelitian ini berhasil mengimplementasikan teknologi terkini, seperti Next.js, React.js dan Sanity, dalam pengembangan sistem pemesanan dan pengiriman makanan. Integrasi yang sukses dengan layanan pembayaran Stripe menunjukkan kecakapan dalam menghadirkan Solusi yang efisien dan aman. Kesimpulannya, penelitian ini mencapai tujuannya dalam menciptakan sistem yang efisien, terintegrasi, dengan potensi pengembangan lebih lanjut untuk menjawab tuntutan pasar yang dinamis.
Kebijakan Satu Peta dan Satu Data dalam Program Percepatan Pengadaan Informasi Geospasial Dasar dan Informasi Geospasial Tematik (Kerja Sama Badan Informasi Geospasial Dengan Badan Usaha Milik Negara) Ulfiah, Ulfiah; Amran Koto, Eryus; Ningsih, Sari
ANTASENA: Governance and Innovation Journal Vol. 2 No. 1 (2024): Juni
Publisher : FIA Unkris Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61332/antasena.v2i1.171

Abstract

One of the important things that is often overlooked in the development process is the utilization of geospatial information as one of the most important data in supporting planning, implementation, monitoring and evaluation. If this is not considered carefully, it will certainly cause damage to the environment and less than optimal All of them are interrelated, connected, influence and influenced, so a single geospatial information is needed that can be used as a reference. Government policy through Presidential Regulation No. 23 of 2021 concerning the Acceleration of One Map Policy Implementation, provides a mandate to increase the Map Accuracy Level from Scale 1:50,000 to Scale 1:5,000
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.
PELATIHAN PENGEMBANGAN MATERI PEMBELAJARAN INTERAKTIF BERBASIS TEKNOLOGI Ningsih, Sari; Gunawan, Arie; Fauziah; Hindarto, Djarot; Yulianto, Lili Dwi; Desmana, Satriawan
Abdi Implementasi Pancasila:Jurnal Pengabdian kepada Masyarakat Vol 4 No 2 (2024): November
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/abdi.v4i2.7802

Abstract

Pelatihan pengembangan materi pembelajaran interaktif berbasis teknologi bertujuan untuk meningkatkan kompetensi guru MTS Asyafi’iyah 04 Jakarta dalam mengintegrasikan teknologi ke dalam proses pembelajaran. Kegiatan ini dilatarbelakangi oleh kebutuhan mendesak untuk mempersiapkan guru dalam menghadapi tantangan era digital dan memastikan pembelajaran yang relevan serta efektif bagi siswa. Metode yang digunakan dalam pelatihan ini meliputi workshop, simulasi, dan evaluasi. Workshop dirancang untuk memberikan pemahaman dasar mengenai teknologi pendidikan dan aplikasinya. Simulasi dilakukan untuk memberikan pengalaman langsung dalam mengembangkan dan menggunakan materi pembelajaran interaktif. Evaluasi dilakukan untuk menilai pemahaman dan kemampuan guru setelah mengikuti pelatihan. Hasil dari pelatihan ini menunjukkan peningkatan yang signifikan dalam kemampuan guru dalam menggunakan teknologi untuk membuat materi pembelajaran interaktif. Selain itu, terdapat peningkatan motivasi dan keterlibatan guru dalam proses pembelajaran. Pelatihan ini diharapkan dapat menjadi model bagi institusi pendidikan lainnya dalam upaya meningkatkan kualitas pembelajaran melalui integrasi teknologi.
Implementation Convolutional Neural Network for Visually Based Detection of Waste Types Wedha, Bayu Yasa; Sholihati, Ira Diana; Ningsih, Sari
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3427

Abstract

Waste detection plays an essential role in ensuring efficient waste management. Convolutional Neural Networks are used in visual waste detection to improve waste management. This study uses a data set that covers various categories of waste, such as plastic, paper, metal, glass, trash, and cardboard. Convolutional Neural Networks are created and trained with refined architecture to achieve precise classification results. During the model development stage, the focus is on utilizing transfer learning techniques to implement Convolutional Neural Networks. Utilizing pre-trained models will speed up and improve the learning process by enriching the representation of waste features. By using the information embedded in the trained model, the Convolutional Neural Network can differentiate the specific attributes of various waste categories more accurately. Utilizing transfer learning allows models to adapt to real-world scenarios, thereby improving their ability to generalize and accurately identify waste that may exhibit significant variation in appearance. Combining these methodologies enhances the ability to identify waste in diverse environmental conditions, facilitates efficient waste management, and can be adapted to contemporary needs in environmental remediation. The model evaluation shows satisfactory performance, with a recognition accuracy of about 73%. Additionally, experiments are conducted under authentic circumstances to assess the reliability of the system under realistic circumstances. This study provides a valuable contribution to the advancement of waste detection systems that can be integrated into waste management with optimal efficiency.
Online Tutoring's Technological Foundation and Future Prospects: Enterprise Architecture Development Ningsih, Sari; Wedha, Bayu Yasa; Sholihati, Ira Diana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3433

Abstract

This study examines the advancement of enterprise architecture with the objective of enhancing the technological infrastructure and long-term strategies in the online student tutoring sector. Online tutoring has emerged as the primary option for supporting the learning process in the rapidly advancing digital age. Identify the essential elements involved in establishing robust groundwork for an online tutoring platform, with a focus on highlighting the strategic significance of enterprise architecture. Examining the technological infrastructure that is customized to fulfill the demands of the tutoring sector constitutes the research methodology utilized in this investigation. Enterprise architecture serves as the fundamental framework that enables smooth integration among different systems, applications, and services used in online tutoring. Creating an enterprise architecture will subsequently generate a well-defined technology roadmap, empowering tutoring companies to innovate with greater precision. This architecture enhances the role of online tutoring in providing a more adaptable and personalized learning experience for students by utilizing advanced technologies like artificial intelligence and data analytics. This study emphasizes the significance of enterprise architecture in facilitating educational transformation and establishing a robust framework for online tutoring companies to progress efficiently. To foster the growth and advancement of the online tutoring industry, it is crucial to strategically enhance the technological infrastructure and implement a well-designed enterprise architecture. This will enable the sector to play a substantial role in shaping a dynamic and forward-thinking educational landscape.
The Impact of Big Data on Enterprise Architectural Design: A Conceptual Review Sholihati , Ira Diana; Wedha, Bayu Yasa; Ningsih, Sari; Sari, Ratih Titi Komala
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3449

Abstract

A conceptual analysis of the impact of big data on enterprise architecture design is provided in this article. Within the framework of expanding digitalization, big data has emerged as a pivotal component in delineating the strategy and framework of organizations. The objective of this study is to investigate the ways in which big data can impact and facilitate the growth of efficient enterprise architecture. Qualitative analysis is the method utilized by researchers to comprehend the intricacies of the interaction between enterprise architecture and big data. This article examines several facets by conducting an extensive review of the literature, including the ways in which big data can facilitate the enhancement of analytical capabilities, innovation in business processes, and strategic decision-making. Emerging challenges, including data security, privacy, and the necessity for IT infrastructure adaptation, are also considered in this study. The outcomes of the review indicate that the implementation of big data in enterprise architecture may substantially alter business strategies and operations. These encompass enhanced system adaptability, customized service provision, and predictive functionalities. Nonetheless, these modifications necessitate modifications to privacy policies, risk management, and data governance. This study presents novel findings regarding the influence of big data on enterprise architecture and provides researchers and practitioners with recommendations for developing and executing successful big data strategies. This research thereby enhances the current body of literature and offers practical guidance in the field.
Co-Authors Achmad Pratama Rifai Adinta, Brema Adisti Suryaningtyas Putri Wirawan Aditya Nur Rohman Aditya Nuryudha Iriandi Agam Beny Styawan Agung Triayudi Ahmad Deni Maulana Ahmad Muslih Mardia Ahmad Rafiansyah Fauzan Ahmad Rifqi Alfath Yauma Alfin Syaifudin Alfin Syaifudin Alica Dwi Fahira Amran Koto, Eryus Andrianingsih Anis Supriatin Ariana Azimah Arie Gunawan Arie Gunawan Aris Gunaryati Ariwirawan Djali Arman Mubarokh Asrul Sani Ayun Sriatmi Azhiman, Muhammad Fauzan Bagas Dwi Ardianto Bayu Anggara Bayu Yasa Wedha Bayu Yasa Wedha Budyarti, Sisca Chuy Mandala Putra Cintia Marito Sihombing Deny Hidayatullah Deny Hidayatullah Deny Hidayatullah Desmana, Satriawan Dhema, Salestinus Petrus Dhieka Avrilia Lantana Dhieka Avrilia Lantana Dicke Rifki Fajrin Dinda Nurkhaliza Putri Djoko Widodo Eky Pambudi Syiamtoni Endah Tri Esti Handayani Eri Mardiani Eri Mardiani Erina Rahmazani Fachry, Fachry Fajrin, Dicke Rifki Fauziah Fauziah Fauziah Ferdiansyah Ferdiansyah Ghina Rahma Guon Fernando Tarimakase Guridno, Ciptoningaji Handayani, Endah Tri Esti Hasbulloh Hasbulloh Hasbulloh, Hasbulloh Hindarto, Djarot Imelta Natalia Ginting Indah Safitri, Ramadanti Indra Lukmana Ira Diana Sholihati Iriandi, Aditya Nuryudha Irmawati Irmawati Iskandar Fitri Iskandar Fitri Iskandar Fitri Iskandar Fitri, Iskandar Junior, Reza Phahlevi Keysha Belynda Tyva Panggabean Lili Dwi Yulianto Lombu, Azzaleya Agashi Mandala Anugrah Putra Maraghi Agil Prabowo Mardia, Ahmad Muslih Mardiani, Eri Moh. Iwan Wahyuddin Mohammad Iwan Wahyuddin Muhammad Mustaqim Muhammad Nurdin Muhammad Rangga Nandila, Alisyafira Sayyidina Nguyen, Huu Tho Nur Hayati Nur Hidayah, Camelia Nur Rahmansyah Nur Rahmansyah Nurfaiz, Kelfin Nurhayati Nurhayati Pamungkasari, Panca Dewi Panca Dewi Pamungkasari Penchala, Sathish Kumar Perdana, Muhammad Rizky Prasetyo, Yoga Dwi Putro, Prayogo Dwi Cahyo Rahmansyah, Nur Ratih Titi Komalasari Ratih Tri Lestari Reynaldo, Yohanes Reza Phahlevi Junior Ridwan Baharudin Ripin, Muhamad Riyantoro Riyantoro Satria Putra Putra Septi Andryana Shalihati, Ira Diana Shinta Dwi Rahayu Sholihati , Ira Diana Sholihati, Ira Diana Sifonne Adi Wijaya Suhartono Suhartono Suhatmojo, Guing Tri Syaiful Syaiful Syiamtoni, Eky Pambudi Tegar Budiman Titih Aji Kurniawan Tri Waluyo Trie Widiarti Ningsih Ucuk Darusalam Ulfiah, Ulfiah Wedha, bayu Yasa Widayaka, Elfady Satya WIJANARKO, SIGIT Willi Akbar Satria Winarsih Winarsih Winarsih Winarsih Wulandana, Nabila Puspita Yauma, Alfath Yohanes Reynaldo Yulianto, Lili Dwi Yuni Latifah Yusriana Chusna Fadilah