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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Tika Jurnal Info Sains : Informatika dan Sains Indonesian Journal of Business, Accounting and Management
Sutikman, Sutikman
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Computer Science & IT Economics, Econometrics & Finance

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From surviving to thriving: A catalyst for MSME creativity Sugiono, Edi; Pallawagau, Andi; Sutikman, Sutikman; Hardini, Resti; Kusumaningrum, Anisa Putri; Sari, Santi Retno
Indonesian Journal of Business, Accounting and Management Vol. 6 No. 2 (2023)
Publisher : Sekolah Tinggi Ilmu Ekonomi Indonesia Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36406/ijbam.v6i02.1332.155

Abstract

This study delves into the vital role of creativity in empowering Micro, Small, and Medium Enterprises (MSMEs) to survive and flourish in an increasingly competitive market landscape. The authors present a comprehensive framework designed to cultivate creativity and innovation within MSMEs, which includes key components such as fostering an entrepreneurial mindset, building robust networks, and ensuring access to essential resources. By implementing this framework, MSMEs can effectively navigate the challenges posed by limited resources and rapidly evolving market conditions. The research underscores the significance of creativity as a driving force behind MSMEs' growth and competitiveness, illustrating how innovative approaches can lead to enhanced performance and sustainability. Furthermore, the study offers valuable insights for a diverse audience, including entrepreneurs seeking to enhance their business strategies, policymakers aiming to create supportive environments for MSMEs, and academics interested in the dynamics of small business development. Ultimately, this research contributes to a deeper understanding of how fostering creativity can catalyze the advancement of MSMEs in Indonesia, promoting economic resilience and innovation in the region. Note: This article serves as a restored version of the original content following a corruption incident. The Digital Object Identifier (DOI) has been successfully re-registered and reactivated to ensure continued accessibility and citation integrity.
Resolving Data Imbalance using SMOTE for the Analysis and Prediction of Hate Speech Sentences Sutikman, Sutikman; Sutanto, Heri; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp198-203

Abstract

Hate speech is characterized as a form of communication that expresses hostility or discontent towards particular individuals, groups, or ethnicities, with the intent to belittle one party. This research aims to examine hate speech expressions on Twitter, assessing their categorization as hate speech through the application of machine learning methodologies. The study incorporates feature engineering techniques, such as Term Frequency-Inverse Document Frequency (TF-IDF) and the Synthetic Minority Over-sampling Technique (SMOTE), to mitigate challenges related to data imbalance. The machine learning models utilized include Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Random Forest (RF). Among these models, Logistic Regression (LR) demonstrated the highest efficacy, achieving an accuracy of 91.43%, precision of 88.83%, recall of 93.99%, and an F1 score of 97.10%.
Binary Classification of Academic Outcomes Using Ensemble Learning and Neural Networks: A Case Study on OULAD Yulianto, Lili Dwi; Satriawan Desmana; Sutikman, Sutikman; Winarsih, Winarsih
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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Abstract

The importance of academic classification in online learning platforms is increasingly recognized as it helps in assessing student performance, early detection of issues, and identifying factors that influence academic success. This study uses the Open University Learning Analytics Dataset (OULAD) to predict students' academic success in various classification areas, including Distinction vs Non-Distinction, Withdrawn vs Non-Withdrawn, Pass vs Non-Pass, and Pass vs Fail. The aim of this research is to compare machine learning and deep learning techniques, such as Random Forest, Gradient Boosting, AdaBoost, LightGBM, and Voting Classifier, with a deep learning model based on Dense Neural Networks (DNN) to produce the best possible predictions. Relevant features are also selected using feature selection and dimensionality reduction strategies, including autoencoders and Recursive Feature Elimination (RFE). The results show that LightGBM and Gradient Boosting perform best in several classifications, with an accuracy of 75.47% for Pass vs Fail. On the other hand, DNN requires further refinement but shows potential in handling more complex classifications. In addition to identifying students at risk of failing, this method provides a deeper understanding of the variables affecting academic success in online learning environments.
Aplikasi sistem informasi perpustakaan Mbaku (Mari Baca Buku) Sutikman, Sutikman; Winarsih, Winarsih
Jurnal Tika Vol 7 No 2 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v7i2.1326

Abstract

Books are a medium for anyone to gain knowledge or dig up information, especially for students. In the learning process, students are often asked to look for references and additional information through books. In the process, of course, the campus provides book lending facilities, namely the library. However, it is often found that the books needed when searched for in the library directly are not available or the stock is inadequate so that it wastes the user's time to come and search in the library with the probability of getting the book that meets expectations is only 50%. Mari Baca Buku (MBAKU) is a web-based application that is used by users to search for information related to books online by getting certainty about the validity of the desired book data before carrying out the process of borrowing books at the library. MBAKU application provides solutions related to the problem of book stock and bridges between one library to another, thus providing access for MBAKU members to be able to borrow books at several libraries in Indonesia that have been integrated with MBAKU. The stages of designing this application include system design, use case design and activity diagrams, database design and interface design design. The database used is MySQL while the programming language uses PHP 7 and TypeScript. The result is a library information system application with all available features and has two user levels and has its own access rights. The two user levels are the user and the administrator.
Co-Authors Aris Puji Widodo Heri Sutanto Kusumaningrum, Anisa Putri Pallawagau, Andi Resti Hardini Santi Retno Sari, Santi Satriawan Desmana Sugiono, Edi Winarsih Winarsih Yulianto, Lili Dwi