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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Tekno Kompak Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Riset Informatika dan Teknologi Informasi (JRITI) Jurnal Komtika (Komputasi dan Informatika)
Yusuf, Ajif Yunizar Pratama
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Meningkatkan Kompetensi Literasi Digital Berbasis Digital Literacy Global Framework (DLGF) Di Global Persada Mandiri Bekasi Muhajirin, Adi; Yusuf, Ajif Yunizar Pratama
Jurnal Pengabdian kepada Masyarakat UBJ Vol. 6 No. 2 (2023): June 2023
Publisher : Lembaga Penelitian Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/ccq15h18

Abstract

Digital literacy is the ability to search, understand and use information from various digital sources wisely. UNESCO has created a framework for measuring digital literacy levels called the Digital Literacy Global Framework (DLGF). That DLGF has instruments consisting of 1) Information and data literacy, 2) Communication and collaboration, 3) Digital Content Creation, 4) Safety, 5) Problem Solving and transformed by KOMINFO into 4 pillars of literacy to support digital transformation, namely 1) Digital Skills, 2) Digital Ethics, 3) Digital Safety and 4) Digital Culture. The purpose of this community service is to measure and improve the digital literacy skills of the Bekasi City community in dealing with hoax news and to analyze the extent of digital literacy competence through the DLGF framework. Where as many as 234,444 participants took part in the activities carried out by cybercreating in the city of Bekasi with a total of 2,564,941 and only about 9% of the population had just participated. for the method used is descriptive method with a quantitative approach. The data collection technique is a questionnaire method and library research (Library Research). The results obtained are an increase in Digital Skill 5.47%, Digital Ethics 8.79%, Digital Safety 7.63%, and Digital Culture 8.47%).
Sentiment Analysis of On-Demand Ride-Hailing Systems using Support Vector Machine and Naïve Bayes Wiguna, Bhagaskara Farhan; Herlawati, Herlawati; Yusuf, Ajif Yunizar Pratama
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7384

Abstract

Gojek is one of Indonesia's most popular online transportation, founded in 2010. The Gojek application has been downloaded one hundred forty-two million times with more than two million drivers and four hundred thousand partners in food delivery services. Due to the increasing use of the Gojek application and the importance of knowing user views about the services provided by the application. In this research, the sentiment analysis is using Support Vector Machine and the Naïve Bayes method to classify positive sentiment and negative sentiment. The target label focus on positive and negative labels to aims avoid the bias that exists in neutrally labeled reviews on the Gojek Application. The research process includes data collection, pre-processing the data, weighting with Term Frequency-Invers Document Frequency, Support Vector Machine, and Naïve Bayes training by dividing the data into 90% training data and 10% testing data and then evaluating the results using a confusion matrix. The results of testing using the Support Vector Machine algorithm resulted in 90% accuracy, 94% recall, 91% precision, and 94% f1-score, therefore the Naïve Bayes algorithm produces 77% accuracy, 96% recall, 77% precision, and 85% f1-score.
Analisis Sentimen Review Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing Arrasyid, Rizky Maulana; Putera, Diaz Enggar; Yusuf, Ajif Yunizar Pratama
Jurnal Tekno Kompak Vol 18, No 2 (2024): AGUSTUS
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v18i2.3813

Abstract

Penelitian ini menganalisis ulasan produk yang ada di pasaran, dengan fokus pada produk "Kaos Oversize", untuk mengklasifikasikannya ke dalam ulasan positif dan negatif. Penelitian ini bertujuan untuk menunjukkan keefektifan penggunaan algoritma K-Nearest Neighbors (KNN) dan Term Frequency-Inverse Document Frequency (TF-IDF) dengan pendekatan Natural Language Processing (NLP) dalam mengklasifikasikan ulasan produk. Penelitian ini menemukan bahwa metode NLP mencapai tingkat akurasi, presisi, dan recall yang lebih tinggi dibandingkan dengan tidak menggunakan NLP. Hasil penelitian menunjukkan bahwa menganalisis kata kunci dalam ulasan dapat mewakili opini keseluruhan pembeli terhadap produk, yang dapat menjadi informasi yang berguna bagi pengecer untuk mengevaluasi produk dan layanan mereka.
Analisis Sentimen Menggunakan Algoritma Logistic Regression Pada Penerbangan Lion Air berdasarkan Ulasan Platform Online Rahmawati, Irma; Rika Fitriani, Tiara; No'eman, Achmad; Yusuf, Ajif Yunizar Pratama
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.60

Abstract

Perkembangan dunia digital telah mendorong masyarakat melakukan pemesanan tiket pesawat menggunakan platform online. Jenis transportasi ini sangat dibutuhkan oleh masyarakat untuk melakukan perjalanan baik antar provinsi maupun antar negara dalam waktu yang relatif singkat. Salah satu maskapai yang paling banyak diminati adalah Lion Air. Hal ini dikarenakan maskapai ini memiliki harga yang relative terjangkau dengan berbagai pilihan kelas penumpang. Namun masakapai penerbangan ini banyak menuai opini diberbagai media sosial sehingga mempegaruhi standar kualitas pelayanan pada maskapai tersebut. Tujuan penelitian ini untuk mengetahui opini positif, negatif dan netral terhadap pelayanan maskapai penerbangan Lion Air berdasarkan opini penumpang maskapai pada platform online. Metode Naïve Bayes, Random Forest dan Logistic Regression digunakan sebagai alat analisis untuk melihat persepsi terhadap maskapai tersebut. Hasil penelitian memperlihatkan tingkat akurasi klasifikasi opini penumpang maskapai penerbangan menggunakan Linear Regression sebesar 0.82 dengan nilai precission sebesar 0.82, recall 0.78, F1 – score 0.80 dan akurasinya 0.82. Sedangkan pada metode Naïve Bayes dan Random Forest masing – masing mendapatkan hasil akurasi 0.47 dan 0.39. Dari hal tersebut bahwa metode klasifikasi menggunakan Linear Regression menunjukkan hasil terbaik.
Analisis Tingkat Kepuasan Pengguna Shopee Bedasarkan Rating Dan Ulasan Google Play Store Menggunakan Naïve Bayes Hadiwibowo, Agus Wastia Tri; Nabilla, Fazhira Putri; Yusuf, Ajif Yunizar Pratama
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 2 (2024): Desember 2023 - Maret 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i2.122

Abstract

Shopee adalah salah satu toko online shop paling populer di Google Play Store. Untuk meningkatkan kualitas dan memberikan pengalaman berbelanja yang lebih baik kepada pembeli atau customer, analisis kepuasan online berdasarkan peringkat Google Play Store menjadi sangat penting bagi para costumer. Tujuan dari penelitian ini adalah untuk melakukan analisis review pengguna terhadap shoppe di Google Play Store untuk mengetahui sentimen positif atau negatif dari pengguna. metode analisis opini yang digunakan adalah Naïve Bayes. Penelitian ini diawali dengan pengumpulan data, preprocessing data, melakukan ekstraksi fitur, melatih model Naive Bayes dan menguji model. Data yang digunakan dalam penelitian sebanyak 1.499 ulasan yang diperoleh melalui review dari google play store. Hasil penelitian menunjukkan bahwa mayoritas pengguna puas dengan shopee. Hal ini terlihat dari sebanyak 40,3% pengguna menyampaikan perasaan positif. Meskipun demikian, penelitian ini juga menemukan indikasi lain bahwa ada beberapa masalah yang perlu diselesaikan. Hasil penelitian ini dapat digunakan pengembang untuk meningkatkan kualitas dan memberikan pengalaman berbelanja yang lebih baik kepada pengguna.
Klasifikasi Penentuan Siswa Berprestasi Menggunakan Algoritma Naïve Bayes Classifier DI PT.Yes Study Education Group Indonesia Laksono, Novan Ponco; Syaaifullah, Achmad Akbar; Yusuf, Ajif Yunizar Pratama
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 3 (2025): April - Juli 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i3.158

Abstract

PT. Yes Study Education Group Indonesia is an overseas education consultancy founded by international alumni and based in Toronto, Canada, with experience helping thousands of students from various parts of the world to achieve their dream of studying abroad. However, it is not easy to study abroad because there are several factors and documents that must be prepared, such as passports, visas, and English test certificates like the Test Of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS). To achieve optimal results, good learning outcomes are required; furthermore, of course, learning outcomes are indicators of student achievement, so an algorithm is needed to determine student performance, with the aim of serving as a supporting tool in evaluating the learning process and outcomes using the naïve bayes classifier algorithm with a trial dataset of 200 student names along with their respective scores, from which 80 test records were obtained. From these calculations, the Gaussian NB model with a 50:50 split validation yielded an accuracy of 73%, scenario 2 with a 60:40 ratio yielded 75% accuracy, scenario 3 with a 70:30 ratio yielded 76.6% accuracy, scenario 4 with an 80:20 ratio yielded 82.2% accuracy, and scenario 5 with a 90:10 ratio yielded 85% accuracy.
Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine Handayanto, Rahmadya Trias; Herlawati, Herlawati; Atika, Prima Dina; Khasanah, Fata Nidaul; Yusuf, Ajif Yunizar Pratama; Septia, Dwi Yoga
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 2 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i2.6280

Abstract

Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. Keywords:
Co-Authors Adi Muhajirin Arrasyid, Rizky Maulana Fata Nidaul Khasanah Hadiwibowo, Agus Wastia Tri Herlawati Herlawati Laksono, Novan Ponco Nabilla, Fazhira Putri No'eman, Achmad Prima Dina Atika Putera, Diaz Enggar Raden Mohamad Herdian Bhakti Rahmadya Trias Handayanto Rika Fitriani, Tiara Septia, Dwi Yoga Syaaifullah, Achmad Akbar Wiguna, Bhagaskara Farhan