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All Journal INTI Nusa Mandiri Bulletin of Computer Science Research Akasia: Artikel Ilmiah Sistem Informasi Akuntansi
Ainun Zumarniansyah
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Computer Science & IT

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PENERAPAN DECISION TREE DENGAN PENYEIMBANGAN DATA IMBALANCE MENGGUNAKAN UPSAMPLING DALAM PREDIKSI PENYAKIT LIVER Agung Fazriansyah; Yuris Alkhalifi; Ainun Zumarniansyah
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6369

Abstract

Acute liver disease has a significant impact on liver function and is often only detected at an advanced stage due to the lack of patient awareness for early examination.  One of the challenges in treating liver disease is the delay in diagnosis, where many patients do not notice the early symptoms until their condition has worsened.  Therefore, a predictive system is needed that can identify liver disease patients early on, allowing for regular check-ups and timely treatment.  In this study, a classification model was developed using a machine learning approach, specifically the Decision Tree algorithm, by balancing the data in the minority class through upsampling.  The research results show that this model is capable of predicting liver disease status with an accuracy rate of 89.22%, a recall of 88.45%, a precision of 83.21%, and an f1-score of 85.78%.  In addition, the ROC-AUC value of 0.89 is categorized as a good classification.  This model achieved a higher accuracy score than other studies with similar datasets.  This system is expected to help improve early detection and expedite the treatment of liver disease patients.
Analisis Sentimen Berita Online Terhadap Transportasi Online di Indonesia dengan Metode Naïve Bayes Classifier, Support Vector Machine dan K-Nearest Neighbor Selawati, Arina; Yan Rianto; Rachmawati Darma Astuti; Ainun Zumarniansyah; Deny Novianti
Bulletin of Computer Science Research Vol. 5 No. 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i2.477

Abstract

News about online transportation in Indonesia in 2019 until early 2020 has been published in various Indonesian online media, because there is enough information in the form of text without numerical scale, it is difficult to classify information information efficiently without reading the full text. Sentiment analysis is used to automate the process of assessing opinion whether it is positive or negative. Classifying sentiments on news from online news media with the Text Mining process and using the method of increasing the Classification Accuracy / Ensemble Method of Engineering by combining the classification algorithm naïve bayes method, classifier Supporting vector machines and k-nearest neighbors added with the Particle Swarm Optimization method and Vote method The next will be a comparative analysis. The results of the study above get an SVM exam accuracy value even after using the PSO selection feature with the ensemble. Select is still appropriate at 84.16%, Likewise for NB algorithm which gets 79.08% and KNN which gets approval 87.19%. These words will be used to see words related to sentiments that often appear and have the highest weight and can be used to find out positive news articles and negative news articles. And for this research the model that uses KNN algorithm gets the highest accuracy.
Penerapan Metode Prototype Rancang Bangun Sistem Informasi Penjualan Mobil Bekas Kredit Pada Mobilindo Pratama Ainun Zumarniansyah; Cahya, Fani Nurona; Pebrianto, Rangga
Akasia: Artikel Ilmiah Sistem Informasi Akuntansi Vol 5 No 1 (2025): Artikel Ilmiah Sistem Informasi Akuntansi (AKASIA) - April 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/sqf0e755

Abstract

Seiring dengan meningkatnya permintaan kendaraan bermotor, terutama mobil bekas yang dibeli secara kredit, kebutuhan akan sistem informasi yang terstruktur dan efisien menjadi sangat penting. Mobilindo Pratama, sebagai salah satu showroom mobil bekas, masih menggunakan sistem manual dalam pencatatan data dan transaksi, yang berdampak pada keterlambatan pelayanan, risiko kesalahan pencatatan, serta ketidakefisienan dalam pembuatan laporan. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi penjualan mobil bekas berbasis komputer dengan menggunakan metode prototype. Metode ini memungkinkan pengguna untuk berinteraksi langsung dengan sistem pada tahap awal, sehingga pengembang dapat memperbaiki dan menyesuaikan sistem berdasarkan masukan yang diberikan. Sistem yang dibangun mencakup fitur pemesanan kendaraan, pengajuan kredit, pelunasan uang muka, pencatatan transaksi, dan pembuatan laporan penjualan. Hasil implementasi menunjukkan bahwa sistem ini dapat membantu mempercepat proses penjualan, meningkatkan akurasi pencatatan data, serta memberikan kemudahan dalam pelaporan. Dengan demikian, sistem informasi ini diharapkan dapat meningkatkan kinerja operasional dan kualitas pelayanan di Mobilindo Pratama secara keseluruhan.
Co-Authors Agung Fazriansyah Arina Selawati Cahya, Fani Nurona Deny Novianti Rachmawati Darma Astuti Yan Rianto Yuris Alkhalifi