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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) REKAYASA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) International Journal of Global Operations Research Jurnal Puan Indonesia Sainteks: Jurnal Sain dan Teknik In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Nasution, Vani Maharani
Universitas Informatika Dan Bisnis Indonesia
Agriculture, Biological Sciences & Forestry Arts Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Other

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PENINGKATAN KETERAMPILAN TEKNOLOGI INFORMASI BAGI SISWA AKUNTANSI DALAM PENYUSUNAN LAPORAN KEUANGAN BERBASIS DIGITAL DI SMK MULTIMEDIA MANDIRI Rizki, Sestri Novia; Jaya , Eko Amri; Fitrianto , Adi; Nasution , Vani Maharani; Rusdin , Febi Ramadhani; Ayu , Ilmi Sila; Hutagalung , Carli Apriansyah; Yusran , Rio Rahmat; Karnadi , Very
PUAN INDONESIA Vol. 7 No. 1 (2025): Jurnal PUAN Indonesia Vol. 7 No. 1 Juli 2025
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v7i1.373

Abstract

Community service activities at SMK Multimedia Mandiri aim to improve information technology skills for students majoring in Accounting in preparing digital-based financial reports. In today's digital era, the ability to use software such as Microsoft Excel and Google Spreadsheets is an important skill that must be possessed by prospective workers in the accounting field. This activity was carried out at SMK [School Name] and involved students of grades XI and XII majoring in Accounting as participants. The implementation method was in the form of interactive training, demonstrations, and direct practice in preparing digital financial reports including general journals, ledgers, trial balances, and profit and loss reports. The results of the activity showed an increase in participants' understanding and skills in operating spreadsheet applications and preparing financial reports efficiently and systematically. This service is expected to support students' readiness in facing the needs of the world of work that demands mastery of information technology in accounting practices.
ANALISIS KOMPARATIF ALGORITMA SUPERVISED LEARNING UNTUK KLASIFIKASI SENTIMEN MULTICLASS TREN KENDARAAN LISTRIK Lestari, Verra Budhi; Rizki, Sestri Novia; Nasution, Vani Maharani
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 14, No 1 (2026): Jurnal Tikomsin, Vol 14, No.1, April 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v14i1.1100

Abstract

This research is motivated by the high complexity of public opinion regarding electric vehicle (EV) trends, which can no longer be adequately represented through binary classification; however, a gap remains in the literature regarding the most efficient multiclass classification models within this domain. The study aims to conduct a comparative analysis of Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Naive Bayes (MNB), and K-Nearest Neighbor (KNN) to determine the best model based on accuracy, precision, recall, and computational efficiency. Data consisting of 1,517 textual public opinions from social media were processed through stages including data cleaning, tokenization, stopword removal, and TF-IDF feature extraction. The results indicate that SVM achieved the best performance with an accuracy of 0.781 and an F1-score of 0.595, reflecting model stability and a good balance between precision and recall. Logistic Regression demonstrated superior precision (0.843) but lower recall, while MNB showed good computational efficiency despite moderate performance. Conversely, KNN yielded the lowest performance due to limitations in handling high-dimensional and sparse data. Further analysis reveals that all models struggled with the neutral class, indicating data imbalance and class similarity. This study contributes to the limited literature on multiclass sentiment evaluation in the EV domain and provides strategic insights into the trade-offs between model complexity, efficiency, and performance. These findings serve as a foundation for developing effective sentiment analysis systems to support decision-making related to electric vehicle trends.
Co-Authors Adi Fitrianto Amos Duan Nugroho Ariawan, Angga Budiman Cecep Kurniawan Eko Amri Jaya Ginanjar, Ari Ramdan Graha Prakarsa Hutagalung , Carli Apriansyah Ilmi Sila Ayu Karnadi , Very Marwondo Neli, Mery Ulfa Nursyanti, Reni Permana, Alwi Dahlan Ramadhiana, Irena Nurul Reni Nursyanti Rizki, Sestri Novia Rusdin , Febi Ramadhani Safira Noor Andinia Tombak Gapura Bhagya Verra Budhi Lestari Yunaidi, Andri Yusran , Rio Rahmat