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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi
Devia Kartika
Universitas Putra Indonesia YPTK Padang, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Analisa Sistem Informasi Pemliharaan Prasarana Jalan dan Jembatan Dinas Pekerjaan Umum (PU) Firna Yenila; Mutiana Pratiwi; Devia Kartika; Rima Liana Gema; Gustiawan Efendi
Jurnal Teknologi Vol. 9 No. 1 (2019): Jurnal Teknologi
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (476.642 KB) | DOI: 10.35134/jitekin.v9i1.6

Abstract

Telah dilakukan penelitian pada Kantor Dinas Pekerjaan Umum (PU) Kabupaten Pasaman Baratdimana permasalahan  yang  dijadikan  sebagai  dasar  dalam  penelitian  ini  yaitu penyajian  informasi  Mengenai prasarana  Jalan  Dan   jembatansecara  cepat  dan  tepat.  Dengan  proses  observasi   secara   langsung kelapangan   telah   mendapatkan   kelemahan   sistem   yang   sedang   berjalan.   Walaupun   tidak   secara keseluruhan   namun   lebih   mengarah   pada   masalah   spesifik,   tetapi   diharapkan dapat   membantu permasalahan  dalam penyajian  informasi  prasarana  jalan  dan  jembatan. Penulis  merancang  sistem informasi dalam  pemeliharaan  jalan  dan  jembatankarena  dengan  sistem  informasi  ini  akan  lebih  cepat, tepat  dan  akurat  dalam penyajianinformasi  kepada pihak-pihak  berkepentingan  dalam  Dinas  Pekerjaan Umum (PU) Kabupaten Pasaman Barat
Optimizing Naive Bayes for Sentiment Analysis of M-Passport Reviews Using N-Gram and Synthetic Minority Over-sampling Technique Devia Kartika; Sarjon Defit
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5853

Abstract

The diverse user perceptions and increasing number of negative reviews of the M-Passport application indicate the need for sentiment analysis-based evaluation to more accurately measure the quality of digital immigration services. This study aims to analyze user sentiment towards the M-Passport application using an optimized Naïve Bayes classification model. Review data was obtained through web scraping from various digital platforms and processed using text preprocessing, TF-IDF feature extraction, N-Gram representation, and the Synthetic Minority Over-sampling Technique (SMOTE) technique to address data representativeness. The proposed model classifies user reviews into positive, neutral, and negative sentiment categories. Test results show that optimization using N-Gram and SMOTE successfully improved model performance, with accuracy increasing from 61% to 77.51%, precision from 0.75 to 0.78, recall from 0.53 to 0.78, and F1-score from 0.50 to 0.77. These results demonstrate that the combination of feature engineering and data balancing can improve text context representation and sentiment classification stability across multiple classes. Furthermore, sentiment analysis successfully identified key factors contributing to user dissatisfaction, such as technical constraints, feature limitations, and application difficulty. These results demonstrate that the proposed approach is effective in supporting data-driven evaluation to improve the quality of digital immigration services.
Co-Authors Firna Yenila Gema, Rima Liana Gustiawan Efendi Mutiana Pratiwi Sarjon Defit