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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Jurnal Informatika Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi AMIKOM ICT AWARD 2010 Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Buana Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Dinamika Informatika CommIT (Communication & Information Technology) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Teknologi Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) JUITA : Jurnal Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Jurnal Inspiration KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Creative Information Technology Journal SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Ilmiah Universitas Batanghari Jambi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Journal of Electrical Technology Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal InComTech: Jurnal Telekomunikasi dan Komputer Insect (Informatics and Security) : Jurnal Teknik Informatika Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Komtika (Komputasi dan Informatika) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang Applied Information System and Management Jurnal Sinergitas PkM & CSR Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah RESEARCH : Computer, Information System & Technology Management INTECOMS: Journal of Information Technology and Computer Science JurTI (JURNAL TEKNOLOGI INFORMASI) Angkasa: Jurnal Ilmiah Bidang Teknologi Jiko (Jurnal Informatika dan komputer) CARADDE: Jurnal Pengabdian Kepada Masyarakat CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) Journal on Education JURTEKSI Jurnal Informasi dan Komputer Multitek Indonesia : Jurnal Ilmiah Jurnal Manajemen Informatika Jambura Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EXPLORE ComTech: Computer, Mathematics and Engineering Applications CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmiah Sinus Informasi Interaktif Majalah Ilmiah Bahari Jogja CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika TAFAQQUH: Jurnal Hukum Ekonomi Syariah Dan Ahwal Syahsiyah Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah JURNAL TAHURI SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Aisyah Journal of Informatics and Electrical Engineering Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics TAJDID KURVATEK Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Tecnoscienza Respati Jurnal Teknika IT (INFORMATIC TECHNIQUE) JOURNAL JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Intelligent Decision Support System (IDSS) G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems Jurnal Sistem Komputer dan Informatika (JSON) Journal of Innovation Information Technology and Application (JINITA) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Bulletin of Computer Science and Electrical Engineering (BCSEE) Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) TEPIAN Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Mitra Mahajana: Jurnal Pengabdian Masyarakat Journal of Applied Computer Science and Technology (JACOST) Jurnal Pendidikan dan Teknologi Indonesia International Journal of Computer and Information System (IJCIS) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) International Research on Big-data and Computer Technology (IRobot) Bulletin of Computer Science Research INFOSYS (INFORMATION SYSTEM) JOURNAL J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal Automation Computer Information System (JACIS) Jurnal Ekonomi dan Teknik Informatika International Journal Artificial Intelligent and Informatics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram EXPLORE Journal of Comprehensive Science Techno SENTRI: Jurnal Riset Ilmiah Indonesian Journal Computer Science (ijcs) Jurnal Educative: Journal of Educational Studies Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer JURNAL TEKNIK INDUSTRI STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Pendidikan Indonesia (Japendi) Cerdika: Jurnal Ilmiah Indonesia International Journal of Advanced Science Computing and Engineering Innovative: Journal Of Social Science Research J-Icon : Jurnal Komputer dan Informatika Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Teknomatika: Jurnal Informatika dan Komputer Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Tafaqquh : Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah Explore The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Teknologi KOPEMAS International Journal of Information Engineering and Science At-Tahdzib: Jurnal Studi Islam dan Muamalah semanTIK JESICA Jurnal Sistem Informasi Komputer dan Teknologi Informasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Komtika (Komputasi dan Informatika)
Ema Utami
Universitas Amikom Yogyakarta
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PENERAPAN TEKNIK KOMPUTER FORENSIK UNTUK PENGEMBALIAN DAN PENGAHAPUSAN BERKAS DIGITAL Anggit Dwi Hartanto; Ema Utami; Hanif Al Fatta
Jurnal Teknologi Vol 4 No 1 (2011): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknologi Industri, Universitas AKPRIND Indonesia

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

Abstract

Penggunaan berkas digital selain banyak kelebihan juga banyak kekurangan antara lain, seringnya dokumen hilang dikarenakan banyak kemungkinan, misalnya menghapus berkas penting secara tidak sengaja. Solusi dari permasalahan itu adalah data yang hilang dikembalikan dengan menggunakan software recovery file. Tetapi dengan adanya solusi tentang pengembalian data yang telah hilang tersebut juga menimbulkan masalah baru yaitu bagaimana cara agar data yang dihapus tidak bisa dikembalikan lagi dengan software recovery file yang ada. Solusi dari kedua masalah tersebut adalah dengan dilakukan percobaan yang bersifat try and error yaitu dilakukannya tindakan-tindakan terhadap data atau berkas digital. Dengan mengacu pada tindakan tersebut akan diketahui hasil dari penggunaan software testdisk versi 6.11 dalam pengembalian data dan penghapusan data. Kata Kunci: recovery file, kehilangan data, forensik
Comparative Analysis of ADDIE and ASSURE Models in Designing Learning Media Applications Hasna Nirfya Rahmandhani; Ema Utami
Jurnal Educative: Journal of Educational Studies Vol. 7 No. 2 (2022): December 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/educative.v7i2.6005

Abstract

Learning media is an important part of producing quality learning. Therefore, learning media development is needed that supports the learning objectives. Several models can be used in designing learning media, such as the ADDIE and ASSURE models. The ADDIE model has the following development steps: (1) Analysis, (2) Design, (3) Development, (4) Implementation, and (5) Evaluation. While the ASSURE model has the following development steps: (1) Analyzing learners, (2) Stating learning objectives, (3) Selecting media and methods, (4) Utilizing media and methods, (5) Requiring learner response, (6) Evaluating. The purpose of this study is to compare the use of the ADDIE model and ASSURE model according to the learning objectives. The research was conducted using the literature review method from 36 articles and books that discuss the making of learning media with ADDIE and ASSURE design models. The literature review references were taken from Google Scholar with publication years between 2012 - 2022. The results of this study show that the use of the ADDIE design model is more effective in designing learning media applications because it focuses on material content and application feasibility so that students can use it optimally to achieve goals. Meanwhile, the ASSURE model is more effective for designing learning systems or teaching materials because itfocuses on the application of learning media design models that can attract student interest to generate learning motivation.
ANALISIS SENTIMEN: PREDIKSI RATING TERHADAP REVIEWS WISATAWAN TANJUNG PUTING PADA TRIPADVISOR MENGGUNAKAN SUPPORT VECTOR MACHINE Ain, Quratul; Utami, Ema; Nasiri, Asro
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.5430

Abstract

Di era digital, sebagian besar data di internet berbentuk teks mentah. Data tambang emas ini sangat berharga karena berisi banyak informasi mendasar yang dapat diekstraksi menggunakan natural language processing dengan analisis sentimen. Termasuk data Reviews dan rating online di era digital menjadi hal pertimbangan penting dan terpercaya bagi wisatawan sebelum berkunjung ke sebuah objek wisata, salah satunya situs TripAdvisor. Penelitian ini akan memproses data teks ulasan wisatawan pada TripAdvisor pada objek Taman Nasional Tanjung Puting untuk menentukan sentimen wisatawan  berdasarkan rating 1 hingga 5. Data penelitian yang digunakan adalah sebanyak 390 reviews yang telah di-export dengan TripAdvisor Reviews Scrapper. Penelitian ini menggunakan Metode Support vector machine(SVM)  yang dikombinasikan dengan feature extraction dan Truncate singular-value decomposition (TSVD) pada preprocessing data. Penelitian ini dapat menampilkan visualisasi wordcloud top 10 kata yang paling banyak muncul berdasarkan rating sebagai pengetahuan dan informasi bagi pelaku industri pariwisata.  Berdasarkan pengujian dengan pembagian dataset 30:70 tingkat akurasi memperoleh rata-rata 80%.
PENGGABUNGAN K-NEAREST NEIGHBORS DAN LIGHTGBM UNTUK PREDIKSI DIABETES PADA DATASET PIMA INDIANS: MENGGUNAKAN PENDEKATAN EXPLORATORY DATA ANALYSIS Pramudyantoro, Arvi; Utami, Ema; Ariatmanto, Dhani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.4966

Abstract

Diabetes Melitus merupakan masalah kesehatan yang signifikan di seluruh dunia. Dengan menggabungkan algoritma K-Nearest Neighbors (KNN) dan Light Gradient Boosting Machine (LightGBM),penelitian ini menyajikan pendekatan baru untuk meningkatkan prediksi diabetes. Kumpulan data Indian Pima, yang terkenal dengan intrik dan signifikansinya dalam penelitian diabetes, menjadi subjek penelitian ini. Untuk menyelidiki pola dan hubungan dalam data, penelitian ini menggunakan analisis data eksploratif, atau EDA. Pra-pemrosesan data yang komprehensif, yang mencakup pengkodean, normalisasi, dan penanganan nilai yang hilang, adalah yang berikutnya. Karena KNN dan LightGBM cocok dengan fitur kumpulan data ini, maka keduanya dipilih. Performa model dioptimalkan melalui penggunaan teknik pengoptimalan seperti Pencarian Acak dan Pencarian Grid untuk mengubah hyperparameter. Metrik seperti skor F1, kurva ROC, analisis presisi-recall, dan akurasi-presisi digunakan untuk menilai model. Hasilnya menunjukkan peningkatan signifikan dalam keakuratan prediksi diabetes, yang menunjukkan bahwa penggunaan LightGBM bersama dengan KNN dan EDA secara hati-hati dapat meningkatkan akurasi prediksi. Khususnya bila dipertimbangkan dalam konteks data kesehatan yang rumit, temuan ini secara signifikan memajukan deteksi penyakit kronis. Menggunakan kumpulan data Pima Indians, algoritma KNN dan LightGBM bekerja sama untuk mencapai akurasi tertinggi sebesar 90,6%.
IMPLEMENTASI FRAME INTERPOLATION PADA ANIMASI STOPMOTION Moniva, Anip; Utami, Ema; Purwanto, Agus
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 2 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i2.4614

Abstract

Salah satu keterbatasan dalam pembuatan animasi stop-motion adalah kesulitan dalam menciptakan gerakan yang halus dan natural, terutama dalam konteks waktu. Time interpolation adalah proses menghitung bingkai-bingkai perantara dalam urutan waktu, yang memungkinkan transisi yang lebih mulus antara bingkai kunci dalam animasi stop-motion. Dalam penelitian ini, berbagai metode time interpolation dieksplorasi dan diimplementasikan dalam konteks stop-motion. Melalui serangkaian eksperimen yang terkontrol, teknik time interpolation yang berbeda diuji untuk efektivitas dan efisiensi dalam menciptakan gerakan yang lebih lancar. Implementasi ini diuji dalam berbagai skenario yang mencerminkan kondisi produksi nyata. Hasil penelitian menunjukkan peningkatan signifikan dalam kualitas animasi dengan penggunaan time interpolation, dengan pergerakan yang lebih halus dan transisi yang lebih natural antara bingkai. Selain itu, teknik ini juga terbukti mengurangi waktu produksi, membuat proses lebih efisien. Secara keseluruhan, penelitian ini menawarkan wawasan penting dan inovasi dalam bidang animasi stop-motion. Implementasi time interpolation dalam stop-motion bukan hanya meningkatkan estetika visual tetapi juga efisiensi proses produksi, membuatnya relevan dan berharga bagi industri animasi, pendidik, dan peneliti.
Penerapan Algoritma Boyer Moore yang di Modifikasi untuk stemmer Bahasa Indonesia Kasim, Rafli Junaidi; Utami, Ema
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.5449

Abstract

Proses stemming dalam Natural Language Processing (NLP) adalah tahap penting dalam pra-pemrosesan data untuk mengurai bentuk kata menjadi kata dasar. Dalam konteks bahasa Indonesia, proses ini melibatkan penghapusan imbuhan untuk menemukan kata dasar. Beberapa metode stemming yang umum digunakan termasuk Porter stemmer, Lancaster stemmer, Snowball stemmer, dan Nazief Andriani stemmer. Meskipun banyak penelitian telah dilakukan untuk meningkatkan akurasi stemming, penelitian ini menyoroti peran algoritma string matching, terutama algoritma Boyer Moore, dalam mencocokkan hasil stemming dengan kamus kata. Namun, implementasi langsung algoritma Boyer Moore menghadapi kendala karena mencocokkan pattern pada seluruh teks, yang harusnya hanya pada bagian kanan kata. Oleh karena itu, algoritma ini dimodifikasi agar sesuai dengan kebutuhan dan tetap mempertahankan kinerjanya. Studi terdahulu menunjukkan bahwa algoritma Boyer Moore memiliki kinerja yang lebih cepat dibandingkan dengan beberapa algoritma string matching lainnya seperti Knuth Morris Pratt, Brute Force, dan Rabin Karp. Hasil penelitian ini berhasil mencapai tingkat akurasi sebesar 95,2% dari total 500 kata yang diproses. Hasil dari penilitian ini juga menunjukan kesalahan stemming yang terjadi hanya diakibatkan dari understemming dan beberapa yang kata tidak ter-stemming.
Deteksi Cyberbullying Menggunakan BERT dan Bi-LSTM Farasalsabila, Fidya; Utami, Ema; Hanafi, Hanafi
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4636

Abstract

Cyberbullying is a digital problem that is not a new phenomenon. This existed before the advent of social networks, and cyberbullying has a wide impact, including a person's mental and physiological conditions such as sadness, anxiety and depression. The main objective of this research is to develop an effective cyberbullying detection system using natural language processing techniques. The method used in this research includes the application of the BERT (Bi-Directional Encoder Representations from Transformers) and Bi-LSTM (Bi-Directional Long Short-Term Memory) models as a deep learning approach to analyze text and detect cyberbullying behavior. This approach allows the system to understand complex language contexts and capture patterns that traditional methods may find difficult to identify. Testing was carried out using a dataset that included various types of Indonesian language texts containing cyber bullying acts. The research results show that the combination of BERT and Bi-LSTM is able to provide superior detection performance with a high accuracy rate of 90% and the ability to identify variations of cyber bullying. This research makes a significant contribution to efforts to protect individuals from the negative impacts of cyber bullying through the development of a sophisticated and adaptive detection system.
Literature Study on the Development of Neural Networks For Weather Forecasting Amir, Fail; Utami, Ema; Hanafi, Hanafi
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4637

Abstract

Weather prediction has always been crucial for individuals to make informed decisions and protect themselves from potential hazards. Achieving accurate weather forecasts has historically been a significant challenge. Modern weather forecasting has evolved to integrate sophisticated computer models, data from atmospheric balloons and satellites, and insights from local observations. These methods have resulted in fairly precise predictions. Most forecasting models depend on complex mathematical formulas, but Artificial Neural Networks (ANN) offer a dynamic alternative, adapting their structure based on incoming data. This research aimed to thoroughly evaluate the effectiveness of ANNs in weather prediction. It explored the advantages of ANNs over traditional models, reviewed a range of methodologies, and documented the latest advancements in the field. The ultimate goal was to consolidate research findings to highlight the strides made in enhancing weather forecasting through ANNs.
Detect Fake Reviews Using Random Forest and Support Vector Machine Hadi, Zulpan; Utami, Ema; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12090

Abstract

With the rapid development of e-commerce, which makes it possible to buy and sell products and services online, customers are increasingly using these online shop sites to fulfill their needs. After purchase, customers write reviews about their personal experiences, feelings and emotions. Reviews of a product are the main source of information for customers to make decisions to buy or not a product. However, reviews that should be one piece of information that can be trusted by customers can actually be manipulated by the owner of the seller. Where sellers can spam reviews to increase their product ratings or bring down their competitors. Therefore this study discusses detecting fake reviews on productreviews on Tokopedia. Where the method used is the distribution post tagging feature to perform detection. By using the post tagging feature method the distribution got 856 fake reviews and 4478 genuine reviews. In the fake reviews, there were 628 reviews written with the aim of increasing product sales or brand names from store owners, while there were 228 reviews aimed at dropping their competitors or competitors. Furthermore, the classification is carried out using the random forest algorithm model and the support vector machine. By dividing the dataset for training data by 80% while 20% for data testing. Here it is known that the support vector machine gets much higher accuracy than the random forest. The support vector machine gets an accuracy of 98% while the random forest gets an accuracy of 60%
Sentiment Analysis of Shopee Food Application User Satisfaction Using the C4.5 Decision Tree Method Fersellia, Fersellia; Utami, Ema; Yaqin, Ainul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12531

Abstract

Sentiment analysis on public opinion regarding the shopee food application is an interesting topic in the context of evaluating service quality in the shopee food application. In this digital era, user opinion has a very important role in shaping public perception of the application. Therefore, sentiment analysis is needed to understand user opinion about the shopee food application. This study uses Decision Tree C4.5 to analyze public sentiment on the use of the Shopee Food application on Twitter users. However, beforehand it is necessary to overcome the problem of data imbalance which is common in datasets, where the number of positive, negative, and neutral sentiments is not balanced. To overcome this problem, three different techniques are used, namely SMOTE, undersampling, and a combination of oversampling and undersampling. The results of this study indicate that the SMOTE technique provides better results in overcoming data imbalances and increasing prediction accuracy. With an accuracy of 0.88. the SMOTE technique can provide more accurate sentiment predictions than the undersampling technique and the combination of oversampling and undersampling. This is because SMOTE can synthetically expand the number of minority samples, thereby preventing the loss of information and maintaining variation in the dataset. In conclusion, sentiment analysis on the Shopee Food application on Google Play using the Decision Tree C4.5 algorithm and the SMOTE technique can overcome data imbalances with a prediction accuracy of 0.88. This technique is more efficient than the undersampling technique and the combination of oversampling and undersampling. These results can provide developers with valuable insights to improve app quality and user satisfaction.
Co-Authors , Anggit Dwi Hartanto A.A. 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