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All Journal Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat
Rohman, Adib Annur
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Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Social Sciences

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Pengembangan Aplikasi Kasir dan Sistem Absensi Terintegrasi untuk Meningkatkan Efisiensi Operasional di Warmindo Khoirunnisa, Emila; Saputra, Rama Eka; Manurung, Ayub Michaelangelo; Afridiansyah, Rahmanda; Rezaroebojo, Rizal; Candra, Rejka Aditya; Rohman, Adib Annur; Ramadhan, Ahnaf Irfan; Zeniarja, Junta
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2032

Abstract

Perkembangan ekonomi global yang pesat telah menyebabkan peningkatan permintaan akan makanan, terutama di sektor kuliner. Warung makan Warmindo, salah satu bentuk Usaha Kecil dan Menengah (UKM) di bidang kuliner, menjadi pilihan yang populer di kalangan mahasiswa. Untuk bertahan dan berkembang di pasar yang kompetitif ini, warung makan Warmindo perlu beradaptasi dengan kemajuan teknologi. Aplikasi kasir sangat penting untuk manajemen keuangan yang efisien dan kepuasan pelanggan. Teknologi yang semakin maju memudahkan bisnis untuk bekerja dengan cepat dan efisien. Aplikasi kasir menawarkan fitur-fitur seperti penghitungan, pencatatan, dan laporan keuangan, sehingga meminimalisir kecurangan dan meningkatkan produktivitas karyawan. Selain itu, aplikasi absensi yang terintegrasi dapat membantu dalam pengolahan data dan pemantauan kehadiran, meningkatkan disiplin karyawan. Penelitian ini menerapkan aplikasi kasir dan sistem absensi terintegrasi pada Restoran WARMINDO NOCTURNAL di Semarang Barat, Kota Semarang, Jawa Tengah. Aplikasi ini bertujuan untuk mengoptimalkan proses penjualan dan transaksi, serta pemantauan kehadiran karyawan yang terintegrasi.
Perbandingan Algoritma NBC, SVM, Logistic Regression untuk Analisis Sentimen Terhadap Wacana KaburAjaDulu di Media Sosial X Rohman, Adib Annur; Trisnapradika, Gustina Alfa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7261

Abstract

This research aims to analyze sentiment towards KaburAjaDulu discourse on X social media by utilizing Logistic Regression, Support Vector Machine (SVM), and Naive Bayes algorithms. Data was collected through a crawling process and resulted in 3,011 tweet data. Pre-processing stages include data cleaning, conversion of letters to lowercase, normalization, tokenization, stopword removal, and stemming. After preprocessing, the data was divided into two sentiment categories, namely positive and negative using a lexicon approach. The dataset is divided using an 80:20 scheme for training and test data, with feature representation utilizing the TF-IDF method. The modeling process is performed utilizing the three algorithms to be evaluated using accuracy, precision, recall, and f1-score metrics. As a solution to class inequality, the oversampling technique SMOTE (Synthetic Minority Over-sampling Technique) is applied. Based on the evaluation, it shows that before the application of SMOTE, Naive Bayes algorithm obtained 78.18% accuracy, 81.80% precision, 77.06% recall, and 77.35% f1-score; SVM obtained 85.63% accuracy, 86.49% precision, 85.68% recall, and 85.94% f1-score; while Logistic Regression obtained 83.05% accuracy, 85.31% precision, 82.47% recall, and 82.95% f1-score. After applying SMOTE, Naive Bayes improved to 81.90% accuracy, 82.27% precision, 81.67% recall, and 81.87% f1-score; SVM obtained 85.63% accuracy, 87.59% precision, 86.89% recall, and 87.13% f1-score; and Logistic Regression obtained 83.33% accuracy, 84.46% precision, 83.62% recall, and 83.88% f1-score. These findings prove that SVM has the most consistent and superior sentiment classification performance on this dataset, making an important contribution to the development of methods for analyzing people's views on social media platforms.
Co-Authors Afridiansyah, Rahmanda Candra, Rejka Aditya Junta Zeniarja Khoirunnisa, Emila Manurung, Ayub Michaelangelo Rama Eka Saputra Ramadhan, Ahnaf Irfan Rezaroebojo, Rizal Trisnapradika, Gustina Alfa