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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Computer and Technology
Nurkholis, Lalu Moh.
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Computer Science & IT

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Menerapkan Metode First Expired First Out Pada Sistem Informasi Persediaan Obat Nurkholis, Lalu Moh.; Arwidiyarti, Dwinita; Haryati, Sri
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 9, No 2 (2024): November 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcit.v9i2.23834

Abstract

Pengelolaan persediaan obat yang tepat sangat penting bagi apotek untuk meningkatkan efisiensi operasional dan mencegah kerugian akibat penumpukan obat kedaluwarsa. Masalah sering terjadi karena kurangnya perhatian terhadap tanggal kedaluwarsa saat obat dikeluarkan untuk penjualan. Penelitian ini bertujuan untuk meningkatkan efisiensi pengelolaan persediaan obat melalui penerapan Metode First Expired First Out (FEFO), yang ideal untuk produk dengan masa simpan terbatas. Metode ini memastikan obat dengan masa kedaluwarsa terdekat digunakan terlebih dahulu, sehingga dapat meminimalkan kerugian. Untuk mendukung penerapan FEFO, dikembangkan sistem informasi persediaan obat berbasis web menggunakan metode pengembangan perangkat lunak Waterfall, yang meliputi analisis, perancangan, pengkodean, implementasi, dan evaluasi. Analisis kebutuhan mencakup aspek fungsional dan non-fungsional. Perancangan dilakukan dengan alat bantu seperti Diagram UML (Use Case dan Aktivitas) untuk dua aktor utama, yaitu admin dan kasir. Perancangan basis data menggunakan Entity Relationship Diagram menghasilkan tujuh tabel yang saling berelasi. Penelitian ini menghasilkan sistem informasi yang mampu menghasilkan laporan stok obat, obat masuk, dan obat keluar sesuai periode yang diinginkan. Pengujian fungsional menggunakan Metode Black Box Testing menunjukkan hasil 100% berhasil, di mana seluruh fitur berfungsi sesuai spesifikasi.Proper inventory management of medicines is essential for pharmacies to improve operational efficiency and prevent losses caused by the accumulation of expired medicines. Problems often arise due to a lack of attention to expiration dates when medicines are issued for sales. This study aims to enhance the efficiency of medicine inventory management through the implementation of the First Expired First Out (FEFO) method, which is ideal for products with limited shelf life. This method ensures that medicines nearing their expiration date are used first, minimizing potential losses. To support the implementation of FEFO, a web-based medicine inventory information system was developed using the Waterfall software development method, which includes analysis, design, coding, implementation, and evaluation phases. System requirements analysis covered both functional and non-functional aspects. The system design utilized tools such as UML diagrams (Use Case and Activity Diagrams) for two main actors, namely the admin and cashier. Database design using an Entity Relationship Diagram produced seven interrelated tables. This study resulted in an information system capable of generating reports on medicine stock, incoming medicines, and outgoing medicines for a specified period. Functional testing using the Black Box Testing method showed 100% success, with all features functioning as expected.
FAKE REVIEW DETECTION ON DIGITAL PLATFORMS USING THE ROBERTA MODEL: A DEEP LEARNING AND NLP APPROACH Hadi, Zulpan; Nurkholis, Lalu Moh.; Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi
Journal Computer and Technology Vol. 3 No. 1 (2025): Juli 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i1.355

Abstract

Fake reviews have emerged as a serious threat to the integrity of digital platforms, particularly in e-commerce and online review sites. This study explores the application of RoBERTa (Robustly Optimized BERT Approach), a transformer-based architecture optimized for natural language processing (NLP), in automatically detecting fake reviews. The methodology includes data collection from online platforms, contextual feature extraction using RoBERTa embeddings, model training through supervised learning, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The training results indicate a significant convergence trend in the training loss, while the validation loss remains relatively unstable, reflecting challenges in model generalization. Nevertheless, experimental results demonstrate that RoBERTa outperforms other approaches such as Logistic Regression PU, K-NN with EM, and LDA-BPTextCNN, achieving an accuracy of 86.25%. These findings highlight RoBERTa's strong potential in detecting manipulative content and underscore its value as an essential tool in building a transparent and trustworthy digital ecosystem.
Co-Authors Arwidiyarti, Dwinita Imran, Bahtiar Selamet Riadi Sri Haryati Suryadi, Emi Zulpan Hadi