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All Journal Jurnal Media Infotama JSAI (Journal Scientific and Applied Informatics) Jurnal Teknik Informatika (JUTIF)
Khanza, Muthia
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Computer Science & IT

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Implmentasi Algoritma Apriori Untuk Meningkatkan Penjualan Handphone di Toko Mardha Cell Khanza, Muthia; Toyib, Rozali Toyib; Onsardi, Onsardi
JSAI (Journal Scientific and Applied Informatics) Vol 4, No 2 (2021): Juni 2021
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v4i2.1674

Abstract

Pemesanan barang oleh toko yang tidak terencana menyebabkan banyaknya penumpukkan barang dari jenis tertentu saja dan kekurang stok barang untuk produk yang lainnya karena tidak melihat prilaku pembelian yang terjadi setiap waktu tertentu sehingga banyak terjadi transaksi yang tidak bisa dilakukan menyebabkan kerugian yang besar bagi usaha dalam meningkatkan pemasaran barang dan terjadi stok berlebihan, dengan menggunakan Algoritma Apriori yang menyatukan teknik pembelajaran mesin  dan pengolahan statistik dan database  yang memungkinkan pengambilan informasi database yang besar dari data transaksi-transaksi yang dilakukan (prilaku Konsumen) dalam pembelian produk sehingga memungkinkan bagi took untuk menyediakan produk-produk yang diminati konsumen porsinya lebih banyak berdasarkan nilai support dan confidence minimum berdasarkan kesamaan antar produk. Berdasarkan hasil pengujian selama 6 bulan terakhir dengan minimum support dan confidence type yang banyak dibeli C15, 8apro = A12 dengan nilai confidence 80,43% serta A1k = A12 dengan nilai confidence 85,71%,  kelebihan algoritma ini adalah mempunyai kemampuan komputasi yang lebih besar dan kelemahannya harus selalu dilakukan tahap scanning yang berulang di setiap iterasinya membutuhkan waktu yang lamaKeywords:Booking,Stock, Apriori Algorithm, Support, confidence
A MODEL KONSEPTUAL E-COMMERCE TOKO BANGUNAN MENGGUNAKAN PENDEKATAN ENTERPRISE ARCHITECTURE SCORE CARD (EA SCORE CARD) Handayani, Sri; Khanza, Muthia; Putra, Eko Manggara
Jurnal Media Infotama Vol 17 No 2 (2021)
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v17i2.1642

Abstract

This E-Commerce System Changes Consumer Behavior From Online Shopping, So The Availability Of Well-Formatted Data, In One Well-Managed Data Source, Is Also A Goal Of Organizational Development. To Realize This, It Is Necessary To Choose An Accurate Strategy And Plan In Developing Enterprise Architecture (EA). You Need To Adopt Or Develop Your Own EA Framework For Enterprise Architecture. Enterprise Architecture Needs To Adapt And Follow Developments In Information And Technology That Are Currently Developing, As A Support For The Smooth Running Of Business. , Logical Organization Of Key Business Processes And Information Technology (IT) Capabilities That Reflect The Need For Integration And Standardization Of Operating Models. Enterprise Architecture Or Enterprise Architecture Is A Description Of The Stakeholder's Mission, In This Case The Leadership Of The Organization, Which Includes Information, Functionality/Usability, Organizational Location And Performance Parameters. Enterprise Architecture. Research Using EA Score Card Calculations For Users Obtained The Following Results: The Director Section With A Percentage Of 54%, The Warehouse Section 1-3 Ranging From 73-76% And The Outlet Section 1-3 With An Average Of 75-84%, Because If The Results If The Overall Architectural Maturity Test Is Achieved > 50% Then The Architecture Is Declared "Feasible", Getting Efficient Features, This Goal Is Achieved By Getting Features That Are Suitable For The Users Involved, Namely, Director, Warehouse And Outlet.
ENHANCING SENTIMENT ANALYSIS OF THE 2024 INDONESIAN PRESIDENTIAL INAUGURATION ON X USING SMOTE-OPTIMIZED NAIVE BAYES CLASSIFIER Afuan, Lasmedi; Khanza, Muthia; Zahira Hasyati, Adila
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4290

Abstract

The inauguration of the President and Vice President of Indonesia for the 2024-2029 period has drawn significant public attention, reflecting widespread political and societal interest. This study aims to optimize sentiment analysis of public opinion on X (formerly Twitter) regarding the inauguration by enhancing the Naïve Bayes Classifier (NBC) with the Synthetic Minority Over-sampling Technique (SMOTE). Addressing the issue of class imbalance in sentiment data, the research demonstrates how SMOTE improves classification robustness. The methodology includes data crawling from X, preprocessing involving tokenization, stemming, and TF-IDF feature extraction, and sentiment labeling using TextBlob. Sentiment classification is conducted with NBC, evaluated under conditions with and without SMOTE. Metrics such as accuracy, precision, recall, and F1-score are utilized to assess performance. Results indicate that the application of SMOTE increases the accuracy of NBC from 98% to 99%, with precision improving from 0.98 to 1 and recall maintaining high levels (0.99). This 1% accuracy enhancement underscores the significance of addressing class imbalance for reliable sentiment analysis. The findings contribute to a better understanding of public sentiment during critical political events and highlight the effectiveness of SMOTE in improving text classification tasks. This research provides valuable insights into leveraging machine learning techniques for analyzing imbalanced datasets, offering implications for both academic and practical applications in sentiment analysis and political studies.
Co-Authors Handayani, Sri Lasmedi Afuan Onsardi, Onsardi Putra, Eko Manggara Toyib, Rozali Toyib Zahira Hasyati, Adila