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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Linguistik Komputasional Jurnal Ilmu Teknik dan Komputer Jurnal Abdidas JUSTIN (Jurnal Sistem dan Teknologi Informasi) Universal Raharja Community (URNITY Journal) Jurnal Indonesia Sosial Teknologi Jurnal Pengabdian Masyarakat Nasional Engagement: Jurnal Pengabdian Kepada Masyarakat Journal Collabits
Afiyati, Afiyati
Universitas Mercu Buana
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture 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 Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Analisis Sentimen Multi-Class Pada Sosial Media Menggunakan Metode Long Short-Term Memory (LSTM) Yuli yuli Astari; Afiyati Afiyati; Saddam Wahib Rozaqi
Jurnal Linguistik Komputasional Vol 4 No 1 (2021): Vol. 4, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v4i1.43

Abstract

Technological developments, especially in the internet and social media, could be a very important research subject in obtaining information, because of the large amount of information in a text found on social media. In recent years, there has been an increase in research about sentiment analysis on text reviews and tweets in order to determine the polarity generated by social media. There are still few studies that apply the deep learning method with the Long Short-Term Memory (LSTM) algorithm to analyze multiclass sentiments in Indonesian-language texts. This study aims to analyze positive and negative emotions in social media texts using the information classification approach in the text and dividing them into 8 different classes using the LSTM method. The dataset is directly taken and collected from users' posts on social media. In testing the LSTM method, the calculation of the accuracy, exactness, review, f-measure values is generated. The results of the processing of the LSTM method show quite well with 5 trials with the highest accuracy value of 91.9% and the average value of multiclass getting 89.45% results.
Challenges of Sarcasm Detection for Social Network : A Literature Review Afiyati Afiyati; Azhari Azhari; Anny Kartika Sari; Abdul Karim
JUITA : Jurnal Informatika JUITA Vol. 8 Nomor 2, November 2020
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1012.329 KB) | DOI: 10.30595/juita.v8i2.8709

Abstract

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.
Pembuatan Website Untuk Deteksi Penyakit Umum Menggunakan Metode Certainty Factor Surya Aji Prasetio; Afiyati Reno
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 7, No 1 (2022): IJCIT Mei 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (534.529 KB) | DOI: 10.31294/ijcit.v7i1.12558

Abstract

Klinik adalah unit kesehatan yang bertanggung jawab menyelenggarakan kesehatan di area kerjanya.  Terbatasnya jam pelayanan dan antrian dapat menyebabkan keterlambatan dalam pengobatan seorang pasien. Untuk itu diperlukan aplikasi yang dapat membantu pasien mendeteksi penyakit secara mandiri sebelum berkonsultasi dengan dokter untuk pengobatan lebih lanjut. Peneltiian ini bertujuan untuk membuat aplikasi berbasis website untuk mendeteksi penyakit secara mandiri yang dilakukan sendiri oleh pasien. Dalam penelitian metode yang digunakan adalah metode Web Development Life Cycle untuk membangun website, dan untuk mendiagnosa penyakit menggunakan metode Certainty Factor. Diagnosa dilakukan untuk penyakit bronkitis, flu burung, dan covid 19. Dalam mendiagnosa penyakit, pasien memilih gejala yang ada. Setelah itu sistem akan memproses, dan amenentukan penyakit dengan nilai tertinggi. Hasil dari metode Certainty Factor ini mencapai 80% dalam menentukan penyakit. Clinic is a health unit that is responsible for providing health in its work area. Limited hours of service and queues can cause delays in the treatment of a patient. For that we need an application that can help patients detect the disease independently before consulting a doctor for further treatment. This research aims to create a website-based application to detect disease independently which is carried out by patients themselves. In the research, the method used is the Web Development Life Cycle method to build a website, and to diagnose diseases using the Certainty Factor method. Diagnosis is made for bronchitis, bird flu, and covid 19. In diagnosing the disease, the patient chooses the existing symptoms. After that the system will process, and determine the disease with the highest value. The results of the Certainty Factor method reach 80% in determining the disease.
Penerapan Algoritma Machine Learning Untuk Penjurusan Siswa Baru Sekolah Menengah Kejurusan Berdasarkan Nilai Raport dan Psikotest Gian Maulana; Afiyati Afiyati
Jurnal Ilmu Teknik dan Komputer Vol 7, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jitkom.2023.v7i1.008

Abstract

Penelitian ini bertujuan untuk mengevaluasi keefektivan sistem penjurusan siswa di Sekolah Menengah Kejuruan (SMK) dengan menggunakan empat algoritma machine learning, yaitu K-Nearest Neighbor, Naive Bayes, Support Vector Machine, dan Random Forest. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki akurasi terbesar dibandingkan algoritma lainnya. Saat ini, proses penjurusan siswa di SMK tempat penelitian ini dilakukan, masih diprosessecara manual melalui perhitungan nilai raport, nilai tes mandiri, dan nilai psikotes. Proses penjuruan secara manual tersebut memakan waktu yang cukup lama. Implementasi algoritma Random Forest dapat menjadi solusi untuk mempercepat proses penjurusan siswa di SMK tersebut. Algoritma Random Forest memiliki akurasi terbaik di antara algoritma lain, yaitu 37% hampir mencapai 38%.
MEDIA PEMBELAJARAN MULTIMEDIA INTERAKTIF BERBASIS ADOBE PREMIERE PRO UNTUK PROSES KEGIATAN PEMBELAJARAN Afiyati Afiyati; Sabar Rudiarto; Anis Cherid
Jurnal Pengabdian Masyarakat Nasional Vol 2, No 2 (2022)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v2i2.18361

Abstract

The purpose of the research was to find out the description of the process of making interactive multimedia based on Adobe Premiere Pro on Basic Competency in Managing the process of teaching and learning activities carried out at SMP Negeri 215 West Jakarta. This type of research uses the ADDIE research model (Analysis, Design, Develop, Implement, and Evaluate), but the author only adopts two stages, namely: analysis and design. This event was attended by 20 West Jakarta SMPN 215 teachers. The implementation of this event uses an approach method, namely presentation of material provided by the speaker, question and answer if there are socialization participants who still do not understand how Adobe Premiere Pro works. Based on the results of the Pretest and Posttest, it shows that after this socialization seminar all teachers can understand the use of Adobe Premiere Pro for making video processing programs that have 45 video effects and 12 audio effects that are used to change display patterns and create video and audio animations that you want to give to students. their students.
Sosialisasi Dampak Kenaikan Beras dengan Prediksi Kebutuhan Beras Masyarakat di Pasar Induk Cipinang dengan Kerjasama Badan Pangan Nasional Santoso, Hadi; Hakim, Lukman; Afiyati, Afiyati; Magdalena, Hilyah
Jurnal Abdidas Vol. 5 No. 2 (2024): April, Pages 64 - 96
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdidas.v5i2.901

Abstract

Kenaikan harga beras yang cukup signifikan pada awal tahun 2024 telah menurunkan daya beli masyarakat. Beras sebagai komoditi pangan pokok sebagian besar masyarakat Indonesia, sehingga kenaikan harga beras beimbas cukup terasa pada perekonomian. Kenaikan harga beras harus dapat diantisipasi untuk meminimalkan dampak ke masyarakat. Pasar Induk Cipinang Jakarta sebagai pasar induk beras terbesar dapat berperan serta untuk mengendalikan dan memprediksi kenaikan harga serta kebutuhan beras masyarakat. Pengabdian masyrakat ini adalah bentuk kerja sama antara tim peneliti dari Fakultas Ilmu Komputer Universitas Mercubuana dengan Badan Pangan Nasional untuk memberikan sosialisasi memanfaatkan data mining dan AI serta pemograman python sebagai upaya meningkatkan akurasi data perubahan data harga beras. Dengan dukungan data mining dan AI diharapkan para pengusaha dan produsen beras di Pasar Cipinang mampu memprediksi stok beras dan berperan serta menjaga stabilitas harga beras.
Sosialisasi Peran Teknologi Artificial Inteligence untuk Klasifikasi Status Sosial Masyarakat DKI Jakarta Hakim, Lukman; Santoso, Hadi; Yusuf, Mohamad; Afiyati, Afiyati
Jurnal Abdidas Vol. 5 No. 3 (2024): June, Pages 97 - 300
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdidas.v5i3.902

Abstract

Pemerintah diberikan amanah dalam mengayomi masyarakat serta memiliki peran melindungi, melayani, dalam kehidupan masyarakat, Provinsi DKI Jakarta merupakan provinsi yang menjadi Ibukota tidak luput dari kesenjangan sosial serta kehidupan masyarakat kota yang perlu perhatian serta peran aktif dalam kebijakan. Dinas  Pemberdayaan Perlindungan Anak dan Pengendalian Penduduk (PPA dan PP) yang memiliki Pusat Data Informasi  keluarga memiliki tugas menyelenggarakan pengumpulan, pengolahan dan penyajian data individu dan keluarga serta pengelolaan sistem informasi individu dan keluarga. Permasalahan perlu adanya aplikasi kecerdasan buatan untuk mengolah data keluarga berdasarkan jenis rumah yang dimiliki untuk menentukan status social masyarakat. Tujuan memberikan sosialisasi peran teknologi kecerdasan buatan berbasis pengolahan citra untuk klasifikasi rumah.Sasaran pada staf pusat data dan Informasi keluarga untuk staf dari Pusdatin PPA dan PP. Metode melakukan pemaparan materi , diskusi, review materi dan evaluasi kegiatan pengmas.berdasarkan hasil pemaparan dan sosialisasi pada PPA dan PP tingkat harapan  3.48 skala 4 dan kenyataan 3.45, secara keseluruhan pemberian sosialisasi diterima dan puas.
Analisis Sentimen Ulasan Pengguna Aplikasi M-Banking Menggunakan Algoritma Support Vector Machine dan Decision Tree Zelina, Nur; Afiyati, Afiyati
Jurnal Linguistik Komputasional Vol 7 No 1 (2024): Vol. 7, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v7i1.169

Abstract

Advances in technology and information have a major influence on human life. The use of this technology has been widely used by humans, especially the use of internet technology. The internet that can be used at an affordable price and easily available supporting hardware has brought humans into a more modern era. In this study, sentiment analysis was carried out on the use of the Motion Banking application using the Support Vector Machine (SVM) algorithm and the Decision Tree algorithm. This study uses the Knowledge Discovery in Database (KDD) method. The purpose of this study is to classify review data from users of the Motion Banking application into positive and negative sentiments by studying user opinions about the Motion Banking application through the reviews provided, and to determine the performance of the classifier method used. In this study, data was obtained by collecting data from user reviews of the Motion Banking application on the Google Play Store using scraping techniques and managed to get 7000 review data. The best results were obtained in scenario 3 (70:30) using the Support Vector Machine algorithm with the Linear kernel which produced 93.7% accuracy, 93.6% precision, 91% recall, and 92.3% f1 score, while for The Decision Tree has an accuracy of 83%, a precision of 80.7%, a recall of 77%, and an f1 score of 79.1%.
A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction Santoso, Hadi; Hanif, Ilham; Magdalena, Hilyah; Afiyati, Afiyati
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1943

Abstract

The categorization of waste plays a crucial part in efficient waste management, facilitating the recognition and segregation of various waste types to ensure appropriate disposal, recycling, or repurposing. With the growing concern for environmental sustainability, accurate waste classification systems are in high demand. Traditional waste classification methods often rely on manual sorting, which is time-consuming, labor-intensive, and prone to errors. Hence, there is a need for automated and efficient waste classification systems that can accurately categorize waste materials. In this research, we introduce an innovative waste classification system that merges feature extraction from a pretrained EfficientNet model with Principal Component Analysis (PCA) to reduce dimensionality. The methodology involves two main stages: (1) transfer learning using the EfficientNet-CNN architecture for feature extraction, and (2) dimensionality reduction using PCA to reduce the feature vector dimensionality. The features extracted from both the average pooling and convolutional layers are combined by concatenation, and subsequently, classification is performed using a fully connected layer. Extensive experiments were conducted on a waste dataset, and the proposed system achieved a remarkable accuracy of 99.07%. This outperformed the state-of-the-art waste classification systems, demonstrating the effectiveness of the combined approach. Further research can explore the application of the proposed waste classification system on larger and diverse datasets, optimize the dimensionality reduction technique, consider real-time implementation, investigate advanced techniques like ensemble learning and deep learning, and assess its effectiveness in industrial waste management systems.
DIGITAL LITERACY PROGRAM DAILY LIFE WITH AI TOOLS Bambang Jokonowo; Hadi Santoso; Afiyati Afiyati
Jurnal Pengabdian Masyarakat Nasional Vol 4, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v4i2.29638

Abstract

The "Digital Literacy Program: Daily Life with AI Tools" is a community service initiative aimed at enhancing digital literacy by integrating artificial intelligence (AI) tools into daily routines. Conducted at Rumah Pertubuhan Masyarakat Indonesia (PERMAI) in Pulau Pinang, Malaysia, this program seeks to democratize access to AI technologies, fostering a foundational understanding that bridges the gap between complex AI concepts and their practical applications in everyday life. By equipping participants with the skills to utilize AI tools effectively, the program not only improves efficiency in personal and professional activities but also empowers individuals with the knowledge to navigate the evolving digital landscape. The innovative approach of this program is its focus on making AI accessible to a broader audience, promoting digital inclusivity and literacy. Through hands-on workshops and real-world applications, participants learn to integrate AI into tasks such as time management, data organization, and problem-solving, leading to enhanced productivity and informed decision-making. This initiative ultimately contributes to the broader goal of fostering a digitally literate society capable of leveraging emerging technologies for personal and collective advancement.
Co-Authors Abdul Karim Anis Cherid Anny Kartika Sari Arrachman, Abdul Kholiq Ayu, Kurnia Gusti Azhari Azhari Bambang Jokonowo Faaza Naima Gian Maulana Hadi Santoso Hadi Santoso Hanif, Ilham Hilyah Magdalena Irfan Rasyid Muchtar Karisma Riskinanti Kurnia, Rizki Ade Lestari, Mulyati Lukman Hakim Mohamad Yusuf Ningrum, Rahma Farah Novrizal, Kevin Roza, Yuni Sabar Rudiarto Saddam Wahib Rozaqi Sakhrassalam, Haytsam Siti Maesaroh Sukur, Daan Surya Aji Prasetio Syafiq, Nur Muhammad Zihni Yuli yuli Astari Zelina, Nur