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All Journal Semesta Teknika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL ILMIAH INFORMATIKA JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Informatika Kaputama (JIK) Jurnal Mantik JIKA (Jurnal Informatika) Jurnal Abdimas Komunikasi dan Bahasa J-Intech (Journal of Information and Technology) Jurnal Ilmu Komputer JURNAL KESEHATAN, SAINS, DAN TEKNOLOGI (JAKASAKTI) Jurnal Pengabdian Masyarakat Bangsa Jurnal Manuhara: Pusat Penelitian Ilmu Manajemen Dan Bisnis
Antonius Yadi Kuntoro
Universitas Bina Sarana Informatika
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Journal : JURNAL ILMIAH INFORMATIKA

 1 
ANALISIS KEPUASAN PENGGUNA APLIKASI GOPAY MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN K-FOLD CROSS VALIDATION Usnah, Asmaul; Hasan , Fuad Nur; Kuntoro, Antonius Yadi
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10276

Abstract

The rapid advancement of digital technology has significantly increased the adoption of digital wallet services in Indonesia, one of which is the GoPay application. This study aims to analyze user satisfaction with GoPay based on user reviews from the Google Play Store. The classification method used is the Naïve Bayes algorithm, with model validation performed using the K-Fold Cross Validation technique. A total of 3,000 reviews were collected through web scraping and then preprocessed using several text preprocessing steps including cleansing, case folding, tokenizing, stopword removal, and stemming. The data was automatically labeled using the IndoBERT model and classified into two satisfaction categories. The classification results show that the Naïve Bayes algorithm achieved an accuracy of 92.46%, with a precision of 92.25%, recall of 94.70%, and an f1-score of 93.46%. Validation using 10-fold cross-validation resulted in an average accuracy of 92.23%. These results indicate that the model demonstrates strong classification performance and stable generalization on unseen data. This research is expected to contribute to improving GoPay's service quality and serve as a reference for the implementation of machine learning techniques in user satisfaction analysis.
KLASIFIKASI CITRA WADAH MINUMAN REUSABLE DAN NON-REUSABLE MENGGUNAKAN MOBILENETV2 Ramanda, Dea; Hasan, Fuad Nur; Kuntoro, Antonius Yadi
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10349

Abstract

Single-use plastic waste, particularly from beverage bottles, remains a significant contributor to the increasing volume of waste in Indonesia. The limited use of reusable beverage containers underscores the urgent need for technological innovations that can support efficient waste segregation. Addressing this issue, the present study proposes a computer vision-based image classification system designed to automatically distinguish between reusable and non-reusable drinking containers. This research adopts a quantitative experimental approach, employing the MobileNetV2 architecture through transfer learning techniques. The model was trained with augmented and normalized datasets to enhance its generalization across diverse image inputs. Evaluation results demonstrate strong classification performance, achieving 96% accuracy, 99% precision (for tumblers), 95% recall, and a 97% F1-score. These outcomes indicate the effectiveness of MobileNetV2 in identifying visual patterns between container types and its potential for deployment in image-driven waste management systems.
PENERAPAN DATA MINING DALAM PENILAIAN KINERJA AKADEMIK SISWA/I SMP YPI PULOGADUNG DENGAN METODE K-MEANS CLUSTERING Nabilatul Adzra, Salsa; Hasan, Fuad Nur; Kuntoro, Antonius Yadi
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10396

Abstract

Improving the quality of education requires an objective, systematic, and data-driven academic performance assessment system. One technological approach that can be used to support this is data mining, specifically the K-Means Clustering method. This studyaims to cluster student academic data based on report card grades for the odd semester of the 2024/2025 academic year using the K-Means algorithm. Data processing was performed using RapidMiner software, with the optimal number of clusters selected at three (K=3) based on the Davies Bouldin Index (DBI) of 0.077. The clustering results form three main categories: Cluster 0 contains 174 students with average academic performance, Cluster 1 contains only one student with the lowest performance, and Cluster 2 contains 107 students with high academic performance. This grouping provides more structured and useful information for schools in designing targeted academic development strategies. This study demonstrates the effectiveness of the K-Means Clustering method in identifying student academic patterns and classifications.
ANALISIS SENTIMEN PROGRAM MAKAN GRATIS PADA PLATFORM X MENGGUNAKAN AGORITMA NAÏVE BAYES Laia , Metodius Modianus; Hasan, Fuad Nur; Kuntoro, Antonius Yadi
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10427

Abstract

The Free Meal Program is one of the government’s strategic policies that has received various public responses, especially on social media Platform X (formerly Twitter). This study aims to analyze the level of public sentiment toward the Free Meal Program on Platform X. The classification method used is the Naïve Bayes algorithm, with model validation performed using the K-Fold Cross Validation technique. A total of 3,600 Indonesian-language tweets relevant to the Free Meal Program were collected through a web scraping process, followed by text preprocessing steps such as case folding, cleaning, tokenizing, stopword removal, and stemming. Data labeling was carried out semi-automatically using the IndoBERT model, and the tweets were then classified into two sentiment categories: positive and negative. The Naïve Bayes model was trained using the TF-IDF representation and tested on a test set comprising 20% of the total dataset. The evaluation results showed that the Naïve Bayes algorithm achieved an accuracy of 86.46%, precision of 86.55%, recall of 95.25%, and an F1-score of 90.77% on 458 test tweets. Validation using 10-fold cross-validation yielded an average accuracy of 86.74%. These results indicate that the Naïve Bayes algorithm demonstrates good classification performance and stable generalization in classifying public sentiment regarding the Free Meal Program. This research is expected to serve as a supporting tool in mapping public opinion based on social media
Co-Authors Adiputra, Mahesa Aditya, Tommy Ali Mustopa, Ali Anasanti, Mila Desi Ani Nuriska Safitri Budiarto, Gunawan Christian, Yoel Dedi Dwi Saputra Dedi Dwi Saputra Dwiza Riana Fahlapi, Riza Febrian, Dio Ferry Syukmana Hasan , Fuad Nur Hasan, Fuad Nur Hasanah, Riyan Latifahul Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Wahono Hermawan, Muhamad Taufik Khiyarana Fayziyah Laia , Metodius Modianus Lasman Effendi M. Rangga Ramadhan Saelan Mahmud Syarif Mawalda Azharah Moh. Arie Hasan Muhammad Syukur Nabilatul Adzra, Salsa Nadia Septia Aisyah Nanjaya, Ahmad Fadhil Nitra, Ridatu Oca Novianti, Deny Nurajijah, Nurajijah Rachimsah, Wildan Ramadhan, Xena Hadi Ramanda, Dea Rianto, Yan Ridatu Oca Nitra Ridatu Oca Nitra Ridatu Ocanitra Riza Fahlapi RIZA FAHLAPI Riza Fahlapi Saputra, Dedi Dwi Siti Nurlela siti nurlela Sugiarto, Hady Sukmawati, Alfiani Sutisna, Alifan Widad Taufik Asra Usnah, Asmaul Wahono, Hermanto