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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Tekinfo | Scientific Journal of Industrial and Information Engineering JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Jurnal Teknik Industri, Bisnis Digital, dan Teknik Logistik: Jurnal Trinistik
Violita Anggraini
Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Implementasi Algoritma Decision Tree (J.48) untuk Memprediksi Resiko Kredit pada BMT Atik Febriani; Violita Anggraini
Tekinfo: Jurnal Ilmiah Teknik Industri dan Informasi Vol 9 No 2 (2021)
Publisher : Program Studi Teknik Industri Universitas Setia Budi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31001/tekinfo.v9i2.904

Abstract

Credit is crucial in financial institutions that affects the growth and development of these institutions. Weak supervision and management in the process of extending credit to customers can lead to high non-performing loans. This problem occured in one of the financial institutions that provides credit to customers, namely BMT X. Data for 2019 showed that there were 600 applications for multipurpose loans. Of these, only about 76% showed good collectability. The condition of credit collectability that is not optimal causes BMT X to spend more to collect installments that must be paid by the debtor directly. This bad credit causes losses to the financial institution. Thus, in providing credit, BMT X must be smart in assessing customer’ feasibility. The purpose of this research is to design credit policies in order to minimize the prediction errors of customers with bad credit category. The technique used in this research is classification data mining with the J.48 algorithm. To measure the effectiveness of an attribute in classifying a data sample set, it is necessary to select the attribute that has the greatest information gain which will be placed at the root node. This research produces six rules with an accuracy level of 80,2% so as it can be used by BMT X to search customer’s feasibility to gain credit. Keywords: Algorithm J.48, data mining, decision tree, credit risk
Smart Monitoring System Pada Pembudidayaan Jamur Tiram Menggunakan Metode Fuzzy Logic Basirun, Arif Reza; Anggraini, Violita; Al Kautsar, Muhammad Nurudduja
Jurnal TRINISTIK: Jurnal Teknik Industri, Bisnis Digital, dan Teknik Logistik Vol 3 No 1 (2024): Maret 2024
Publisher : nter of Execellence (COE) ICT Infrastructure, Smart Manufacture and Digital Supply Chain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/trinistik.v3i1.1333

Abstract

Mushrooms, which are known as food commodities, when cultivated require temperatures between 22-28°C and humidity of 60-80%. Controlling these two factors is very crucial, especially when weather fluctuations occur throughout the day. As a solution, an artificial intelligence-based system was introduced to regulate temperature and humidity. The "fuzzy control" technique in artificial intelligence is an option because of its flexibility and convenience without involving complex mathematical calculations. The Fuzzy Mamdani method is one approach to "fuzzy control" which allows automatic control of temperature and humidity in mushroom cultivation areas. To apply this method, sensors are needed that can provide data to the fuzzy system. This fuzzy system works in four steps: fuzzification, rule determination, inference, and defuzzification. Based on the results of comparative tests with MATLAB software, there is a correctness level of around 26% in the measurement results.
Prediction of Anime Rating with Hybrid Artificial Neural Networks and Convolutional Neural Networks Al Kautsar, M. Nurudduja; Anggraini, Violita; Basirun, Arif Reza; Rifai, Achmad Pratama
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 22, No 1 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v22i1.28390

Abstract

This study proposes an innovative approach to predict anime scores by leveraging a combination of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). Tabular data such as source, number of episodes, type, and genre are incorporated alongside the image representation of anime into a holistic model. Evaluation results on the test set show satisfactory performance, with an average loss value of 0.673, Mean Absolute Error (MAE) of 0.654, and Mean Absolute Percentage Error (MAPE) of 9.44%. Training and validation graphs reflect the model's convergence without significant signs of overfitting or underfitting. The integration of information from both data sources yields a model capable of providing accurate predictions of anime scores, contributing to an understanding of trends and preferences in the anime industry, and opening opportunities for the development of similar models in the field of score prediction or other quality evaluations.
Integrasi Pipeline Big Data NoSQL dan Pemrosesan Bahasa Alami untuk Manajemen Kualitas Berbasis Voice of Customer Violita Anggraini; Muhammad Iqbal Faturohman; Lathiifah Thawafani
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 4 No. 3 (2026): Juli : Jurnal Ilmiah Sains Teknologi dan Informasi
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v4i3.2305

Abstract

The exponential growth of customer review data on e-commerce platforms generates high volume, variety, and velocity of data, necessitating efficient Big Data Analytics approaches beyond the constraints of traditional relational databases. This study aims to analyze product review sentiments to identify customer satisfaction patterns and inconsistencies between star ratings and textual reviews, while evaluating the efficiency of NoSQL databases in managing such non-relational data structures. The method employed involves text mining and Natural Language Processing (NLP), applying data preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction with bigram-with-punctuation modeling, and text classification using the TextBlob library. The unstructured review data is managed using a NoSQL MongoDB database cluster with a scale-out architecture. The results indicate that integrating symbol-only word filtration significantly improves keyword relevance, and n-gram modeling successfully captures negation contexts like "not good". Word cloud visualizations effectively extract dominant keywords in negative reviews (Score 1), highlighting product quality criticisms ("taste", "product") and origin concerns ("made in"). Furthermore, sentiment distribution inconsistencies were detected, wherein low-rating reviews scores 1 and 2 still exhibited 15.1% and 29.2% positive sentiment, respectively. The implications of this research provide strategic recommendations for e-commerce executive management to integrate real-time sentiment monitoring systems for data-driven decision-making, proactive product development, and customer service optimization.
Pengenalan Simulasi Industri Melalui Gamifikasi Pembelajaran Pada Siswa SMA Kabupaten Purbalingga Violita Anggraini; Muhammad Iqbal Faturohman; Famila Dwi Winati; Aiza Yudha Pratama; Halim Qista Karima; Deva Grasella Manalu; Husni Fadhlurrahman; Agustinus Mahardhika
JURPIKAT Vol 7 No 3 (2026)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v7i3.3362

Abstract

Perkembangan dunia industri menuntut sumber daya manusia yang memiliki kemampuan berpikir sistem dan pemahaman terhadap proses industri secara terintegrasi. Namun, pembelajaran di tingkat SMA masih cenderung bersifat konseptual sehingga siswa memiliki keterbatasan dalam memahami dinamika sistem industri dan pendekatan STEM. Kegiatan pengabdian masyarakat bertujuan untuk mengenalkan konsep simulasi industri serta memperkuat literasi STEM siswa SMA melalui pendekatan gamifikasi menggunakan Beer Game. Metode pelaksanaan dilakukan secara partisipatif melalui tahapan persiapan, pengenalan konsep sistem industri dan STEM, pelaksanaan simulasi Beer Game, serta evaluasi kegiatan, dengan sasaran siswa SMA kelas peminatan teknik. Hasil kegiatan menunjukkan bahwa simulasi Beer Game meningkatkan pemahaman siswa terhadap konsep sistem industri dan rantai pasok, mendorong keterlibatan aktif, serta mengembangkan kemampuan berpikir sistem, pengambilan keputusan. Umpan balik peserta menunjukkan tingkat kepuasan yang tinggi terhadap materi dan metode kegiatan. Dengan demikian, gamifikasi Beer Game berpotensi menjadi model pengabdian masyarakat berkelanjutan dalam penguatan literasi STEM di pendidikan menengah.
Co-Authors Achmad Pratama Rifai Agustinus Mahardhika Aiza Yudha Pratama Al Kautsar, M. Nurudduja Al Kautsar, Muhammad Nurudduja Basirun, Arif Reza Deva Grasella Manalu Famila Dwi Winati Faturohman, Muhammad Iqbal Febriani, Atik Husni Fadhlurrahman Karima, Halim Qista Lathiifah Thawafani