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All Journal Jurnal Sistem Informasi Journal Of Informatics And Busisnes
Yohanes Agung Apriyanto
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Analisis Dan Perancangan Data Warehouse Untuk Pengelolaan Stok Barang Pada Cv Aneka Artha Niaga Septi Putri Azzahra; Yohanes Agung Apriyanto; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 1 No. 2 (2023): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v1i2.375

Abstract

CV Aneka Artha Niaga is a developing company which operates in the field of kassemergud vesmuving distributor. Distributor means an actor who distributes finished products from factory production in the form of packaged snacks and drinks to shops that have registered as customers. As a business that operates in the distribution sector, stock management is an important factor in the success of selling the products they offer. From availability to distribution to customers. The large amount of data that is managed makes it difficult for CV Aneka Artha Niaga to process data analysis and make decisions. This research aims to overcome existing problems at CV Aneka Artha Niaga by implementing a data warehouse with the ITL (Extract, Transform, Load) process. The method used is Kimball's nine steps. Data was collected through field observations, interviews, and the company's ERP system. The result of this research is the implementation of a data warehouse which can be the answer to the problems and information that CV Aneka Artha Niaga needs.
ANALISIS SENTIMEN ULASAN APLIKASI DEEPL PADA GOOGLE PLAY DENGAN METODE SUPPORT VECTOR MACHINE (SVM) Septi Putri; Yohanes Agung Apriyanto; Andri Wijaya
Jurnal Sistem Informasi (JUSIN) Vol 4 No 2 (2023): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v4i2.2368

Abstract

Di era globalisasi dan kemajuan teknologi, pertumbuhan penggunaan aplikasi mobile mengalami perkembangan yang pesat. Memahami tingkat kepuasan dan ketidakpuasan pengguna terhadap aplikasi seluler sangatlah penting. Salah satu aplikasi penerjemah online yang sangat populer di Google Play Store adalah DeepL yang menggunakan teknologi kecerdasan buatan untuk menerjemahkan bahasa. DeepL telah menjadi alat yang sangat berguna bagi individu dan organisasi dalam mengatasi hambatan bahasa. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap review pengguna aplikasi DeepL pada platform Google Play Store, dengan menggunakan metode klasifikasi Support Vector Machine. Metode klasifikasi Support Vector Machine (SVM) merupakan salah satu pendekatan klasifikasi dalam ranah pembelajaran terbimbing dalam data mining. Keunggulan SVM terletak pada kemampuannya menangani data input nonlinier dan berdimensi tinggi dengan memanfaatkan fungsi kernel. Pengumpulan data dilakukan dengan teknik Web Scraping dengan menggunakan Python. Hasil penelitian ini menunjukkan tingkat akurasi sebesar 91% yang mencerminkan seberapa baik model Support Vector Machine (SVM) dalam mengklasifikasikan data. Dalam penelitian tersebut diketahui bahwa model memiliki tingkat presisi yang tinggi terutama pada kategori ‘Positif’ sebesar 94% yang menunjukkan kemampuan model dalam mengenali data yang termasuk dalam sentimen positif secara akurat.
PERENCANAAN STRATEGIS SISTEM INFORMASI (STUDI KASUS : CV XYZ) Septi Putri; Afifah Azzahra; Yohanes Agung Apriyanto; Andri Wijaya
Jurnal Sistem Informasi (JUSIN) Vol 5 No 2 (2024): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v5i2.2902

Abstract

In today's rapidly evolving digital era, information systems (IS) play a crucial role in supporting business operations, especially in trading and distribution companies like CV XYZ. CV XYZ specializes in the distribution of fast-moving consumer goods (FMCG), such as snacks, beverages, and confectionery products. To enhance operational efficiency, strategic information system planning is essential to align IS/IT investments with long-term business goals.this study aims to develop an IS strategic plan for CV XYZ to optimize business operations and improve competitiveness. Using a qualitative approach, data collection involved interviews and literature reviews. Analytical methods, including PESTLE, SWOT, and Gap Analysis, were employed to evaluate internal and external factors influencing the company's IS/IT needs.Key findings indicate that CV XYZ requires an integrated information system to manage inventory, transactions, and financial reporting in real time. Recommended strategies include implementing Customer Relationship Management (CRM) systems, enhancing data security, expanding IT infrastructure, and developing cloud-based solutions. The proposed roadmap outlines a phased IS/IT implementation over 3–5 years, emphasizing the integration of existing applications with advanced technologies to support sustainable growth.The study concludes that strategic IS planning enables CV XYZ to address operational challenges, leverage digitalization opportunities, and improve customer satisfaction. Recommendations include enhancing system integration, upgrading cybersecurity measures, and fostering strategic partnerships to achieve competitive advantages and adapt to market dynamics.
Analisis Ulasan Produk Amazon Menggunakan Random Forest Sentimen dan Probabilistic Retrieval Model Septi Putri Azzahra; Afifah Azzahra; Yohanes Agung Apriyanto; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 2 No. 4 (2025): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v2i4.2070

Abstract

In the digital era, product reviews on e-commerce platforms such as Amazon have become an important source of information for consumers and sellers. This study develops a system for sentiment analysis of reviews using the Random Forest algorithm and relevant information retrieval with a TF-IDF-based probabilistic model. The data used includes 568,454 product reviews from Amazon, which are processed through data cleaning, tokenization, lemmatization, and feature extraction stages. Sentiments are classified into positive, negative, and neutral. The Random Forest model shows reliable performance with precision, recall, and F1-score of 0.878. The probabilistic search system successfully sorts relevant reviews with a high level of accuracy, which is evaluated using the Mean Average Precision (MAP) metric of 0.878. The results of this study provide significant contributions to improving the e-commerce user experience and supporting data-driven decision making. The approach used opens up opportunities for further research in the fields of natural language processing and machine learning.
Co-Authors Afifah Azzahra Andri Wijaya Septi Putri Septi Putri Azzahra